Visual descriptors for content-based retrieval of remote sensing images

Visual descriptors for content-based retrieval of remote sensing images

Abstract

In this paper we present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global hand-crafted, local hand-crafted, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes. We conducted all the experiments on two publicly available datasets: the 21-class UC Merced Land Use/Land Cover (LandUse) dataset and 19-class High-resolution Satellite Scene dataset (SceneSat). The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects. It is therefore not obvious in this domain, which descriptor should be employed to describe images having such a variability. Results demonstrate that CNN-based features perform better than both global and and local hand-crafted features whatever is the retrieval scheme adopted. Features extracted from SatResNet-50, a residual CNN suitable fine-tuned on the RS domain, shows much better performance than a residual CNN pre-trained on multimedia scene and object images. Features extracted from NetVLAD, a CNN that considers both CNN and local features, works better than others CNN solutions on those images that contain fine-grained textures and objects.

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ARTICLE TEMPLATE

ontent-Based Image Retrieval (CBIR); Visual Descriptors; Convolutional Neural Networks (CNNs); Relevance Feedback (RF); Active Learning (AL); Remote Sensing (RS)

1 Introduction

The recent availability of a large amount of remote sensing (RS) images is boosting the design of systems for their management. A conventional RS image management system usually exploits high-level features to index the images such as textual annotations and metadata Datta et al. (2008). In the recent years, researchers are focusing their attention on systems that exploit low-level features extracted from images for their automatic indexing and retrieval  Jain and Healey (1998). These types of systems are known as Content-Based Image Retrieval (CBIR) systems and they have demonstrated to be very useful in the RS domain Demir and Bruzzone (2015); Aptoula (2014); Ozkan et al. (2014); Yang and Newsam (2013); Zajić et al. (2007).

The CBIR systems allow to search and retrieve images that are similar to a given query image Smeulders et al. (2000); Datta et al. (2008). Usually their performance strongly depends on the effectiveness of the features exploited for representing the visual content of the images Smeulders et al. (2000). The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects Yang and Newsam (2010); Dai and Yang (2011). It is therefore not obvious in this domain, which descriptor should be employed to describe images having such a variability.

In this paper we compare several visual descriptors in combination with four different retrieval schemes. Such descriptors can be grouped in two classes. The first class includes traditional global hand-crafted descriptors that were originally designed for image analysis and local hand-crafted features that were originally designed for object recognition. The second class includes features that correspond to intermediate representations of Convolutional Neural Networks (CNNs) trained for generic object and/or scene and RS image recognition.

To reduce the influence of the retrieval scheme on the evaluation of the features we investigated the features coupled with four different image retrieval schemes. The first one, that is also the simplest one, is a basic image retrieval system that takes one image as input query and returns a list of images ordered by their degree of feature similarity. The second and the third ones, named pseudo and manual Relevance Feedback (RF), extend the basic approach by expanding the initial query. The Pseudo RF scheme uses the most similar images to the initial query, for re-querying the image database. The final result is obtained by combining the results of each single query. In the manual RF, the set of relevant images is suggested by the user which evaluates the result of the initial query. The last scheme considered is named active-learning-based RF Demir and Bruzzone (2015). It exploits Support Vector Machines (SVM) to classify relevant and not relevant images on the basis of the user feedback.

For the sake of completeness, for the first three retrieval schemes we considered different measure of similarity, such as Euclidean, Cosine, Manhattan, and -square, while for the active-learning-based RF scheme we considered the histogram intersection as similarity measure, as proposed by the original authors Demir and Bruzzone (2015).

We conducted all the experiments on two publicly available datasets: the 21-class UC Merced Land Use/Land Cover dataset Yang and Newsam (2010) (LandUse) and 19-class High-resolution Satellite Scene dataset Dai and Yang (2011) (SatScene). Evaluations exploit several computational measures in order to quantify the effectiveness of the features. To make the experiments replicable, we made publicly available all the visual descriptors calculated as well as the scripts for making the evaluation of all the image retrieval schemes 1.

The rest of the paper is organized as follows: Section 2 reviews the most relevant visual descriptors and retrieval schemes; Section  3 describes the data, visual descriptors, retrieval schemes evaluated and the experimental setup; Section 4 reports and analyzes the experimental results; finally, Section 5 presents our final considerations and discusses some new directions for our future research.

Figure 1: Main components of a CBIR system.

2 Background and Related Works

A typical Content-Based Image Retrieval (CBIR) system is composed of four main parts Smeulders et al. (2000); Datta et al. (2008), see Fig. 1:

  1. The Indexing, also called feature extraction, module computes the visual descriptors that characterize the image content. Given an image, these features are usually pre-computed and stored in a database of features;

  2. The Retrieval module, given a query image, finds the images in the database that are most similar by comparing the corresponding visual descriptors.

  3. The Visualization module shows the images that are most similar to a given query image ordered by the degree of similarity.

  4. The Relevance Feedback module makes it possible to select relevant images from the subset of images returned after an initial query. This selection can be given manually by a user or automatically by the system.

2.1 Indexing

A huge variety of features have been proposed in literature for describing the visual content. They are often divided into hand-crafted features and learned features. Hand-crafted descriptors are features extracted using a manually predefined algorithm based on the expert knowledge. Learned descriptors are features extracted using Convolutional Neural Networks (CNNs).

Global hand-crafted features describe an image as a whole in terms of color, texture and shape distributions Mirmehdi, Xie, and Suri (2009). Some notable examples of global features are color histograms Novak, Shafer et al. (1992), spatial histogram Wang, Wu, and Yang (2010), Gabor filters Manjunath and Ma (1996), co-occurrence matrices Arvis et al. (2004); Haralick (1979), Local Binary Patterns (LBP) Ojala, Pietikäinen, and Mänepää (2002), Color and Edge Directivity Descriptor (CEDD) Chatzichristofis and Boutalis (2008), Histogram of Oriented Gradients (HOG) Junior et al. (2009), morphological operators like granulometries information Bosilj et al. (2016); Aptoula (2014); Hanbury, Kandaswamy, and Adjeroh (2005), Dual Tree Complex Wavelet Transform (DT-CWT) Bianconi et al. (2011); Barilla and Spann (2008) and GIST Oliva and Torralba (2001). Readers who would wish to deepen the subject can refer to the following papers Rui, Huang, and Chang (1999); Deselaers, Keysers, and Ney (2008); Liu and Yang (2013); Veltkamp, Burkhardt, and Kriegel (2013).

Local hand-crafted descriptors like Scale Invariant Feature Transform (SIFT) Lowe (2004); Bianco et al. (2015) provide a way to describe salient patches around properly chosen key points within the images. The dimension of the feature vector depends on the number of chosen key points in the image. A great number of key points can generate large feature vectors that can be difficult to be handled in the case of a large-scale image retrieval system. The most common approach to reduce the size of feature vectors is the Bag-of-Visual Words (BoVW) Sivic and Zisserman (2003); Yang and Newsam (2010). This approach has shown excellent performance not only in image retrieval applications Deselaers, Keysers, and Ney (2008) but also in object recognition Grauman and Leibe (2010), image classification Csurka et al. (2004) and annotation Tsai (2012). The idea underlying is to quantize by clustering local descriptors into visual words. Words are then defined as the centers of the learned clusters and are representative of several similar local regions. Given an image, for each key point the corresponding local descriptor is mapped to the most similar visual word. The final feature vector of the image is represented by the histogram of the its visual words.

CNNs are a class of learnable architectures used in many domains such as image recognition, image annotation, image retrieval etc Schmidhuber (2015). CNNs are usually composed of several layers of processing, each involving linear as well as non-linear operators, that are learned jointly, in an end-to-end manner, to solve a particular tasks. A typical CNN architecture for image classification consists of one or more convolutional layers followed by one or more fully connected layers. The result of the last full connected layer is the CNN output. The number of output nodes is equal to the number of image classes Krizhevsky, Sutskever, and Hinton (2012).

A CNN that has been trained for solving a given task can be also adapted to solve a different task. In practice, very few people train an entire CNN from scratch, because it is relatively rare to have a dataset of sufficient size. Instead, it is common to take a CNN that is pre-trained on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories Deng et al. (2009)), and then use it either as an initialization or as a fixed feature extractor for the task of interest Razavian et al. (2014); Vedaldi and Lenc (2014). In the latter case, given an input image, the pre-trained CNN performs all the multilayered operations and the corresponding feature vector is the output of one of the fully connected layers Vedaldi and Lenc (2014). This use of CNNs have demonstrated to be very effective in many pattern recognition applications Razavian et al. (2014).

2.2 Retrieval

A basic retrieval scheme takes as input the visual descriptor corresponding to the query image perfomed by the user and it computes the similarity between such a descriptor and all the visual descriptors of the database of features. As a result of the search, a ranked list of images is returned to the user. The list is ordered by a degree of similarity, that can be calculated in several ways Smeulders et al. (2000): Euclidean distance (that is the most used), Cosine similarity, Manhattan distance, -square distance, etc. Brinke, Squire, and Bigelow (2004).

2.3 Relevance Feedback

In some cases visual descriptors are not able to completely represent the image semantic content. Consequently, the result of a CBIR system might be not completely satisfactory. One way to improve the performance is to allow the user to better specify its information need by expanding the initial query with other relevant images Rui et al. (1998); Hong, Tian, and Huang (2000); Zhou and Huang (2003); Li and Allinson (2013). Once the result of the initial query is available, the feedback module makes it possible to automatically or manually select a subset of relevant images. In the case of automatic relevance feedback (pseudo-relevance feedbackBaeza-Yates, Ribeiro-Neto et al. (1999), the top images retrieved are considered relevant and used to expand the query. In the case of manual relevance feedback (explicit relevance feedback (RF)Baeza-Yates, Ribeiro-Neto et al. (1999), it is the user that manually selects relevant of images from the results of the initial query. In both cases, the relevance feedback process can be iterated several times to better capture the information need. Given the initial query image and the set of relevant images, whatever they are selected, the feature extraction module computes the corresponding visual descriptors and the corresponding queries are performed individually. The final set of images is then obtained by combining the ranked sets of images that are retrieved. There are several alternative ways in which the relevance feedback could implemented to expand the initial query. Readers who would wish to deepen on this topic can refer the following papers Zhou and Huang (2003); Li and Allinson (2013); Rui and Huang (2001).

The performance of the system when relevance feedback is used, strongly depends on the quality of the results achieved after the initial query. A system using effective features for indexing, returns a high number of relevant images in the first ranking positions. This makes the pseudo-relevance feedback effective and, in the case of manual relevance feedback, it makes easier to the user selecting relevant images within the result set.

Although there are several examples in the literature of manual RF Thomee and Lew (2012); Ciocca and Schettini (1999); Ciocca, Gagliardi, and Schettini (2001), since human labeling task is enormously boring and time consuming, these schemes are not practical and efficient in a real scenario, especially when huge archives of images are considered. Apart from the pseudo-RF, other alternatives to manual RF approach are the hybrid systems such as the systems based on supervised machine learning Demir and Bruzzone (2015); Pedronette, Calumby, and Torres (2015). This learning method aims at finding the most informative images in the archive that, when annotated and included in the set of relevant and irrelevant images (i.e., the training set), can significantly improve the retrieval performance Demir and Bruzzone (2015); Ferecatu and Boujemaa (2007). The Active-Learning-based RF scheme presented by Demir et al. Demir and Bruzzone (2015) is an example of hybrid scheme. Given a query, the user selects a small number of relevant and not relevant images that are used as training examples to train a binary classifier based on Support Vector Machines. The system iteratively proposes images to the user that assigns the relevance feedback. At each RF iteration the classifier is re-trained using a set of images composed of the initial images and the images from the relevance feedback provided by the user. After some RF iterations, the classifier is able to retrieve images that are similar to the query with a higher accuracy with respect to the initial query. At each RF iteration, the system suggests images to the user by following this strategy: 1) the system selects the most uncertain (i.e. ambiguous) images by taking the ones closest to the classifier hyperplanes; 2) the system selects the (with ) most diverse images from the highest density regions of the future space.

3 Methods and materials

Given an image database composed of images, the most relevant images of to a given query are the images that have the smallest distances between their feature vectors and the feature vector extracted from the query image. Let us consider and as the feature vectors extracted from the query image and a generic image of respectively. The distance between two vectors can be calculated by using several distance functions, here we considered: Euclidean, Cosine, Manhattan, and -square.

In this work we evaluated:

  1. several visual descriptors as described in Sect. 3.1;

  2. different retrieval schemes as described in Sect. 3.2;

We conducted all the experiments on two publicly available datasets described in Sec. 3.3 for which the ground truth is known.

3.1 Visual descriptors

In this work we compared visual descriptors for content-based retrieval of remote sensing images. We considered a few representative descriptors selected from global and local hand-crafted and Convolutional Neural Networks approaches. In some cases we considered both color and gray-scale images. The gray-scale image is defined as follows: . All feature vectors have been normalized (they have been divided by its -norm):

Global hand-crafted descriptors

  • 256-dimensional gray-scale histogram (Hist L) Novak, Shafer et al. (1992);

  • 512-dimensional Hue and Value marginal histogram obtained from the HSV color representation of the image (Hist H V) Novak, Shafer et al. (1992);

  • 768-dimensional RGB and rgb marginal histograms (Hist RGB and Hist rgbPietikainen et al. (1996);

  • 1536-dimensional spatial RGB histogram achieved from a RGB histogram calculated in different part of the image (Spatial Hist RGB) Novak, Shafer et al. (1992);

  • 5-dimensional feature vector composed of contrast, correlation, energy, entropy and homogeneity extracted from the co-occurrence matrices of each color channel (Co-occ. matr.) Arvis et al. (2004); Hauta-Kasari et al. (1996);

  • 144-dimensional Color and Edge Directivity Descriptor (CEDD) features. This descriptor uses a fuzzy version of the five digital filters proposed by the MPEG-7 Edge Histogram Descriptor (EHD), forming 6 texture areas. CEDD uses 2 fuzzy systems that map the colors of the image in a 24-color custom palette;

  • 8-dimensional Dual Tree Complex Wavelet Transform (DT-CWT) features obtained considering four scales, mean and standard deviation, and three color channels (DT-CWT and DT-CWT L) Bianconi et al. (2011); Barilla and Spann (2008);

  • 512-dimensional Gist features obtained considering eight orientations and four scales for each channel (Gist RGB) Oliva and Torralba (2001);

  • 32-dimensional Gabor features composed of mean and standard deviation of six orientations extracted at four frequencies for each color channel (Gabor L and Gabor RGB) Bianconi et al. (2011); Bianconi and Fernández (2007);

  • 264-dimensional opponent Gabor feature vector extracted as Gabor features from several inter/intra channel combinations: monochrome features extracted from each channel separately and opponent features extracted from couples of colors at different frequencies (Opp. Gabor RGB) Jain and Healey (1998);

  • 580-dimensional Histogram of Oriented Gradients feature vector Junior et al. (2009). Nine histograms with nine bins are concatenated to achieve the final feature vector (HoG);

  • 78-dimensional feature vector obtained calculating morphological operators (granulometries) at four angles and for each color channel (Granulometry) Hanbury, Kandaswamy, and Adjeroh (2005);

  • 18-dimensional Local Binary Patterns (LBP) feature vector for each channel. We considered LBP applied to gray images and to color images represented in RGB  Mäenpää and Pietikäinen (2004). We selected the LBP with a circular neighbourhood of radius 2 and 16 elements, and 18 uniform and rotation invariant patterns. We set and for the LandUse and SceneSat datasets respectively (LBP L and LBP RGB).

Local hand-crafted descriptors

  1. SIFT: We considered four variants of the Bag of Visual Words (BoVW) representation of a 128-dimensional Scale Invariant Feature Transform (SIFT) calculated on the gray-scale image. For each variant, we built a codebook of \num1024 visual words by exploiting images from external sources.

    The four variants are:

    • SIFT: \num1024-dimensional BoVW of SIFT descriptors extracted from regions at given key points chosen using the SIFT detector (SIFT);

    • Dense SIFT: \num1024-dimensional BoVW of SIFT descriptors extracted from regions at given key points chosen from a dense grid.

    • Dense SIFT (VLAD): \num25600-dimensional vector of locally aggregated descriptors (VLAD) Cimpoi et al. (2014).

    • Dense SIFT (FV):\num40960-dimensional Fisher’s vectors (FV) of locally aggregated descriptors Jégou et al. (2010).

  2. LBP: We considered the Bag of Visual Words (BoVW) representation of Local Binary Patterns descriptor calculated on each channel of the RGB color space separately and then concatenated. LBP has been extracted from regions at given key points sampled from a dense grid every 16 pixels. We considered the LBP with a circular neighbourhood of radius 2 and 16 elements, and 18 uniform and rotation invariant patterns Cusano, Napoletano, and Schettini (2015). We set and for the LandUse and SceneSat respectively. Also in this case the codebook was built using an external dataset (Dense LBP RGB).

CNN-based descriptors

The CNN-based features have been obtained as the intermediate representations of deep convolutional neural networks originally trained for scene and object recognition. The networks are used to generate a visual descriptor by removing the final softmax nonlinearity and the last fully-connected layer. We selected the most representative CNN architectures in the state of the art Vedaldi and Lenc (2014); Szegedy et al. (2015); He et al. (2016); Arandjelovic et al. (2016) by considering a different accuracy/speed trade-off. All the CNNs have been trained on the ILSVRC-2015 dataset Russakovsky et al. (2015) using the same protocol as in Krizhevsky, Sutskever, and Hinton (2012). In particular we considered \num4096, \num2048, \num1024 and \num128-dimensional feature vectors as follows Razavian et al. (2014); Marmanis et al. (2016):

  • BVLC AlexNet (BVLC AlexNet): this is the AlexNet trained on ILSVRC 2012 Krizhevsky, Sutskever, and Hinton (2012).

  • BVLC Reference CaffeNet (BVLC Ref): a AlexNet trained on ILSVRC 2012, with a minor variation Vedaldi and Lenc (2014) from the version as described in Krizhevsky, Sutskever, and Hinton (2012).

  • Fast CNN (Vgg F): it is similar to the one presented in Krizhevsky, Sutskever, and Hinton (2012) with a reduced number of convolutional layers and the dense connectivity between convolutional layers. The last fully-connected layer is 4096-dimensional Chatfield et al. (2014).

  • Medium CNN (Vgg M): it is similar to the one presented in Zeiler and Fergus (2014) with a reduced number of filters in the convolutional layer four. The last fully-connected layer is 4096-dimensional Chatfield et al. (2014).

  • Medium CNN (Vgg M-2048-1024-128): three modifications of the Vgg M network, with lower dimensional last fully-connected layer. In particular we used a feature vector of 2048, 1024 and 128 size Chatfield et al. (2014).

  • Slow CNN (Vgg S): it is similar to the one presented in Sermanet et al. (2014) with a reduced number of convolutional layers, less filters in the layer five and the Local Response Normalization. The last fully-connected layer is 4096-dimensional Chatfield et al. (2014).

  • Vgg Very Deep 19 and 16 layers (Vgg VeryDeep 16 and 19): the configuration of these networks has been achieved by increasing the depth to 16 and 19 layers, that results in a substantially deeper network than the ones previously Simonyan and Zisserman (2014).

  • GoogleNet Szegedy et al. (2015) is a 22 layers deep network architecture that has been designed to improve the utilization of the computing resources inside the network.

  • ResNet 50 is Residual Network. Residual learning framework are designed to ease the training of networks that are substantially deeper than those used previously. This network has 50 layers He et al. (2016).

  • ResNet 101 is Residual Network made of 101 layers He et al. (2016).

  • ResNet 152 is Residual Network made of 101 layers He et al. (2016).

Besides traditional CNN architectures, we evaluated the NetVLAD Arandjelovic et al. (2016). This architecture is a combination of a Vgg VeryDeep 16 Simonyan and Zisserman (2014) and a VLAD layer Delhumeau et al. (2013). The network has been trained for place recognition using a subset of a large dataset of multiple panoramic images depicting the same place from different viewpoints over time from the Google Street View Time Machine Torii et al. (2013).

To further evaluate the power of CNN-based descriptors, we have fine-tuned a CNN to the remote sensing domain. We have chosen the ResNet-50 which represents a good trade-off between depth and performance. This CNN demonstrated to be very effective on the ILSVRC 2015 (ImageNet Large Scale Visual Recognition Challenge) validation set with a top 1- recognition accuracy of about 80% He et al. (2016).

For the fine-tuning procedure we considered a very recent RS database  Xia et al. (2017), named AID, that is made of aerial image dataset collected from Google Earth imagery. This dataset is made up of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks and viaduct. The AID dataset has a number of 10000 images within 30 classes and about 200 to 400 samples of size 600 600 in each class.

We did not train the ResNet-50 from the scratch on AID because the number of images for each class is not enough. We started from the pre-trained ResNet-50 on ILSVRC2015 scene image classification dataset Russakovsky et al. (2015). From the AID dataset we have selected 20 images for each class and the rest has been using for training. During the fine-tuning stage each image has been resized to and a random crop has been taken of size. We augmented data with the horizontal flipping. During the test stage we considered a single central crop from the -resized image.

The ResNet-50 has been trained via stochastic gradient descent with a mini-batch of 16 images. We set the initial learning rate to 0.001 with learning rate update at every 2K iterations. The network has been trained within the Caffe Jia et al. (2014) framework on a PC equipped with a Tesla NVIDIA K40 GPU. The classification accuracy of the resulting SatResNet-50 fine-tuned with the AID dataset is 96.34% for the Top-1, and 99.34% for the Top-5.

In the following experiments, the SatResNet-50 is then used as feature extractor. The activations of the neurons in the fully connected layer are used as features for the retrieval of food images. The resulting feature vectors have size 2048 components.

3.2 Retrieval schemes

We evaluated and compared three retrieval schemes exploiting different distance functions, namely Euclidean, Cosine, Manhattan, and -square and an active-learning-based RF scheme using the histogram intersection as distance measure. In particular, we considered:

  1. A basic IR. This scheme takes a query as input and outputs a list of ranked similar images.

  2. Pseudo-RF. This scheme considers the first images returned after the initial query as relevant. We considered different values of ranging between 1 and 10.

  3. Manual RF. Since the ground truth is known, we simulated the human interaction by taking the first actual relevant images from the result set obtained after the initial query. We evaluated performance at different values of ranging between 1 and 10.

  4. Active-Learning-based RF. We considered an Active-Learning-based RF scheme as presented by Demir et al. Demir and Bruzzone (2015). The RF scheme requires the interaction with the user that we simulated taking relevant and not relevant images from the ground-truth.

3.3 Remote Sensing Datasets

Figure 2: Examples from the 21-Class Land-Use/Land-Cover dataset. From the top left to bottom right: agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersectionv, medium density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts.
Figure 3: Examples from the 19-Class Satellite Scene dataset. From the top left to bottom right: airport, beach, bridge, commercial area, desert, farmland, football field, forest, industrial area, meadow, mountain, park, parking, pond, port, railway station, residential area, river and viaduct.

The 21-Class Land Use/Land Cover Dataset (LandUse) is a dataset of images of 21 land-use classes selected from aerial orthoimagery with a pixel resolution of 30 cm Yang and Newsam (2010). The images were downloaded from the United States Geological Survey (USGS) National Map of some US regions. 2. For each class, one hundred RGB images are available for a total of 2100 images. The list of 21 classes is the following: agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersectionv, medium density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts. Some examples are shown in Fig. 2.

The 19-Class Satellite Scene (SceneSat) dataset consists of 19 classes of satellite scenes collected from Google Earth (Google Inc.) 3. Each class has about fifty RGB images for a total of 1005 images  Dai and Yang (2011); Xia et al. (2010). following: airport, beach, bridge, commercial area, desert, farmland, football field, forest, industrial area, meadow, mountain, park, parking, pond, port, railway station, residential area, river and viaduct. An example of each class is shown in Fig. 3.

Differences between LandUse and SceneSat

The datasets used for the evaluation are quite different in terms of image size and resolution. LandUse images are of size pixels while SceneSat images are of size pixels. Fig. 4 displays some images from the same category taken from the two datasets. It is quite evident that the images taken from the LandUse dataset are at a different zoom level with respect to the images taken from the SatScene dataset. It means that objects in the LandUse dataset will be more easily recognizable than the objects contained in the SceneSat dataset, see the samples of harbour category in Fig. 4. The SceneSat images depict a larger land area than LandUse images. It means that the SceneSat images have a more heterogeneous content than LandUse images, see some samples from harbour, residential area and parking categories reported in Fig. 2 and Fig. 3. Due to these differences between the two considered datasets, we may expect that the same visual descriptors will have different performance across datasets, see Sec. 4.

(LandUse) (SceneSat) (LandUse) (SceneSat)
Harbor Forest
Figure 4: Comparison between images of the same classes between LandUse and SceneSat dataset.

3.4 Retrieval measures

Image retrieval performance has been assessed by using three state of the art measures: the Average Normalized Modified Retrieval Rank (ANMRR), Precision (Pr) and Recall (Re), Mean Average Precision (MAP) Manning, Raghavan, and Schütze (2008); Manjunath et al. (2001). We also adopted the Equivalent Query Cost (EQC) to measure the cost of making a query independently of the computer architecture.

Average Normalized Modified Retrieval Rank (ANMRR)

The ANMRR measure is the MPEG-7 retrieval effectiveness measure commonly accepted by the CBIR community Manjunath et al. (2001) and largely used by recent works on content-based remote sensing image retrieval Ozkan et al. (2014); Aptoula (2014); Yang and Newsam (2013). This metric considers the number and rank of the relevant (ground truth) items that appear in the top images retrieved. This measure overcomes the problem related to queries with varying ground-truth set sizes. The ANMRR ranges in value between zero to one with lower values indicating better retrieval performance and is defined as follows:

indicates the number of queries performed. is the size of ground-truth set for each query . is a constant penalty that is assigned to items with a higher rank. is commonly chosen to be . AVR is the Average Rank for a single query and is defined as

where is the th position at which a ground-truth item is retrieved. is defined as:

Precision and Recall

Precision is the fraction of the images retrieved that are relevant to the query

It is often evaluated at a given cut-off rank, considering only the topmost results returned by the system. This measure is called precision at or .

Recall is the fraction of the images that are relevant to the query that are successfully retrieved:

In a ranked retrieval context, precision and recall values can be plotted to give the interpolated precision-recall curve Manning, Raghavan, and Schütze (2008). This curve is obtained by plotting the interpolated precision measured at the 11 recall levels of 0.0, 0.1, 0.2, …, 1.0. The interpolated precision at a certain recall level is defined as the highest precision found for any recall level :

Mean Average Precision (MAP)

Given a set of queries, Mean Average Precision is defined as,

where the average precision for each query is defined as,

where is the rank in the sequence of retrieved images, is the number of retrieved images, is the precision at cut-off in the list (), and is an indicator function equalling 1 if the item at rank is a relevant image, zero otherwise.

Equivalent Query Cost

Several previous works, such as Aptoula (2014), report a table that compares the computational time needed to execute a query when different indexing techniques have been used. This comparison can not be replicated because the computational time strictly depends on the computer architecture. To overcome this problem, we defined the Equivalent Query Cost () that measures the computational cost needed to execute a given query independently of the computer architecture. This measure is based on the fact that the calculation of the distance between two visual descriptors is linear in number of components and on the definition of basic cost . The basic cost is defined as the amount of computational effort that is needed to execute a single query over the entire database when a visual descriptor of length is used as indexing technique. The of a generic visual descriptor of length can be obtained as follows:

where the symbol stands for the integer part of the number , while is set to 5, that corresponds to the length of the co-occurrence matrices, that is the shortest descriptor evaluated in the experiments presented in this work.

4 Results

Features ANMRR MAP P@5 P@10 P@50 P@100 P@1000 EQC
Global
Hist. L 0.816 12.46 36.65 30.17 18.07 13.74 5.96 51
Hist. H V 0.781 15.98 54.22 43.49 23.41 16.84 6.27 102
Hist. RGB 0.786 15.39 51.82 41.83 22.29 16.35 6.14 153
Hist. rgb 0.800 14.34 49.46 38.97 20.88 15.31 6.00 153
Spatial Hist. RGB 0.808 14.36 37.70 31.13 19.09 14.62 5.95 307
Co-occ. matr. 0.861 8.69 19.36 17.20 12.14 10.06 5.74 1
CEDD 0.736 19.89 62.45 52.39 29.54 20.86 6.49 28
DT-CWT L 0.707 21.04 39.64 36.36 26.81 22.11 7.90 1
DT-CWT 0.676 24.53 55.63 48.92 32.52 25.09 7.95 4
Gist RGB 0.781 17.65 45.94 38.97 23.10 17.01 6.09 102
Gabor L 0.766 16.08 44.60 37.28 22.65 17.63 7.11 6
Gabor RGB 0.749 18.06 52.72 44.48 25.71 19.13 7.04 19
Opp. Gabor RGB 0.744 18.76 53.81 44.89 26.18 19.69 6.99 52
HoG 0.751 17.85 48.67 41.88 25.37 19.12 6.18 116
Granulometry 0.779 15.45 39.36 33.31 20.76 16.30 7.15 15
LBP L 0.760 16.82 52.77 45.16 26.34 18.84 6.01 3
LBP RGB 0.751 17.96 58.73 49.83 28.12 19.62 6.07 10
Local hand-crafted
Dense LBP RGB 0.744 19.01 60.10 51.89 29.12 20.30 6.33 204
SIFT 0.635 28.49 53.56 49.40 35.98 28.42 8.26 204
Dense SIFT 0.672 25.44 72.30 62.61 35.51 25.96 7.12 204
Dense SIFT (VLAD) 0.649 28.01 74.93 65.25 38.20 28.10 7.18 5120
Dense SIFT (FV) 0.639 29.18 75.34 66.28 39.09 28.54 7.88 8192
CNN-based
Vgg F 0.386 53.55 85.00 79.73 62.29 50.24 9.57 819
Vgg M 0.378 54.44 86.16 81.03 63.42 50.96 9.59 819
Vgg S 0.381 54.18 86.10 81.18 63.46 50.50 9.60 819
Vgg M 2048 0.388 53.16 85.04 80.26 62.77 50.14 9.52 409
Vgg M 1024 0.400 51.66 84.43 79.41 61.40 48.88 9.50 204
Vgg M 128 0.498 40.94 73.82 68.30 50.67 39.92 9.18 25
BVLC Ref 0.402 52.00 84.73 79.37 61.10 48.96 9.49 819
BVLC AlexNet 0.410 51.13 84.06 78.68 59.99 48.01 9.51 819
Vgg VeryDeep 16 0.394 52.46 83.91 78.34 61.38 49.78 9.60 819
Vgg VeryDeep 19 0.398 51.95 82.84 77.60 60.69 49.16 9.63 819
GoogleNet 0.360 55.86 85.36 80.96 64.71 52.36 9.68 204
ResNet-50 0.358 56.57 88.26 84.00 65.92 52.69 9.73 409
ResNet-101 0.356 56.63 88.49 83.53 65.69 52.83 9.75 409
ResNet-152 0.362 56.03 88.42 83.08 64.65 52.50 9.72 409
NetVLAD 0.406 51.44 83.00 78.59 61.63 49.04 9.29 819
SatResNet-50 0.239 69.94 92.06 89.20 77.23 64.42 9.86 409
Table 1: LandUse Dataset results obtained with a basic retrieval system with the Euclidean distance. The lower is the value of and the better is the performance. For the other metrics is the opposite. The best result is reported in bold.
Features ANMRR MAP P@5 P@10 P@50 P@100 P@1000 EQC
Global hand-crafted
Hist. L 0.728 19.86 37.69 32.61 21.05 15.98 5.21 51
Hist. H V 0.704 23.23 43.98 37.29 23.10 17.05 5.21 102
Hist. RGB 0.722 21.24 40.96 34.71 21.17 16.30 5.20 153
Hist. rgb 0.702 23.03 43.76 37.87 23.31 17.11 5.21 153
Spatial Hist. RGB 0.720 22.21 38.85 33.36 21.81 16.30 5.21 307
Co-occ. matr. 0.822 11.73 21.25 18.16 12.94 11.00 5.19 1
CEDD 0.684 24.15 38.13 34.77 24.65 18.52 5.20 28
DT-CWT L 0.672 23.48 35.90 32.43 24.32 20.20 5.21 1
DT-CWT 0.581 33.16 51.00 45.98 32.99 24.52 5.21 4
Gist RGB 0.706 22.98 41.73 37.31 22.98 16.81 5.19 102
Gabor L 0.685 22.84 40.82 35.63 23.45 19.07 5.21 6
Gabor RGB 0.649 27.00 49.19 43.42 26.92 20.70 5.20 19
Opp. Gabor RGB 0.638 28.08 48.14 42.48 28.61 21.01 5.20 52
HoG 0.724 19.97 40.24 35.31 21.73 15.82 5.20 16
Granulometry 0.717 21.41 39.20 33.60 20.78 17.22 5.21 15
LBP L 0.690 22.61 47.24 40.55 24.06 18.16 5.20 48
LBP RGB 0.664 24.95 50.33 43.98 26.33 19.40 5.20 10
Local hand-crafted
Dense LBP RGB 0.660 24.81 51.12 44.29 26.55 19.67 5.21 204
SIFT 0.559 35.47 59.40 53.22 35.04 25.49 5.20 204
Dense SIFT 0.603 31.29 64.06 55.80 31.70 22.24 5.20 204
Dense SIFT (VLAD) 0.552 35.89 71.30 62.78 36.19 25.03 5.20 5120
Dense SIFT (FV) 0.518 39.44 72.34 64.69 38.84 27.23 5.20 8192
CNN-based
Vgg F 0.408 49.91 71.52 68.98 49.62 33.07 5.21 819
Vgg M 0.419 48.59 71.50 68.27 48.62 32.45 5.21 819
Vgg S 0.416 48.89 71.46 68.62 48.79 32.58 5.20 819
Vgg M 2048 0.431 47.14 71.08 67.52 47.33 31.83 5.21 409
Vgg M 1024 0.443 45.86 70.51 66.61 46.05 31.23 5.21 204
Vgg M 128 0.551 34.54 59.30 54.08 36.05 25.65 5.20 25
BVLC Ref 0.407 50.04 71.22 68.65 49.75 33.15 5.21 819
BVLC AlexNet 0.421 48.52 70.45 66.91 48.22 32.51 5.20 819
Vgg VeryDeep 16 0.440 46.18 70.67 66.71 46.22 31.46 5.20 819
Vgg VeryDeep 19 0.455 44.34 69.17 64.65 44.84 30.66 5.20 819
GoogleNet 0.324 60.36 85.73 82.28 68.32 55.75 9.75 204
ResNet-50 0.329 60.32 88.67 85.51 69.44 55.43 9.79 409
ResNet-101 0.327 60.37 88.81 85.10 68.81 55.63 9.79 409
ResNet-152 0.332 59.80 88.55 84.93 67.94 55.39 9.78 409
NetVLAD 0.371 56.37 82.54 78.41 64.40 52.19 9.48 819
SatResNet-50 0.207 74.19 92.11 90.55 80.91 68.02 9.87 409
Table 2: SatScene dataset results obtained with a basic retrieval system with the Euclidean distance. The lower is the value of and the better is the performance. For the other metrics is the opposite. The best result is reported in bold.
(a)
(b)
Figure 5: Per class precision at 5 of a selection of visual descriptors for each dataset. (a) LandUse. (b) RS.
(a) (b)
Figure 6: Interpolated 11-points precision-recall curves of a selection of visual descriptors for each dataset. (a) LandUse. (b) SatScene.

4.1 Feature evaluation using the basic retrieval scheme

In this section we compare visual descriptors listed in Sec. 3.1 by using the basic retrieval scheme. In order to make the results more concise, in this section we show only the experiments performed employing the Euclidean distance. Given an image dataset, in turn, we used each image as query image and evaluated the results according to the metrics discussed above, i.e. , , , , , , and . In the case of the LandUse dataset we performed 2100 queries while in the case of SatScene dataset we evaluated 1005 queries in total.

The results obtained on the LandUse dataset are showed in Table 1, while those obtained on the SatScene dataset are showed in Table 2. Regarding the LandUse dataset, the best results are obtained by using the CNN-based descriptors and in particular the ResNet CNN architectures and the SatResNet-50 that is the fine-tuned ResNet-50. The global hand-crafted descriptors have the lowest performance, with the co-occurrence matrices being the worst one. The local hand-crafted descriptors achieve better results than global hand-crafted descriptors but worse than CNN-based descriptors. In particular, the SatResNet-50, compared with Bag of Dense SIFT and DT-CWT, achieves an value that is lower of about 50%, a value that is higher of about 50% , a that is higher of about 50%, a value that is higher of about 50%. The same behavior can be observed for the remaining precision levels. In particular, looking at we can notice that only the SatResNet-50 descriptor is capable of retrieving about 65% of the existing images for each class (). Regarding the SatScene dataset, the best results are obtained by the CNN-based descriptors and in particular the ResNet CNN architectures and the SatResNet-50. The global hand-crafted descriptors have the lowest performance, with the co-occurrence matrices being the worst one. The local hand-crafted descriptors achieve better results than global hand-crafted descriptors but worse than CNN-based descriptors. In particular the SatResNet-50, compared with Bag of Dense SIFT (FV), achieves an value that is lower of about 60%, a value that is higher of about 50% , a that is lower of about 20%, a value that is higher of about 30%. Similar behavior can be observed for the remaining precision levels. In particular, looking at we can notice that only SatResNet-50 is capable of retrieving about 70% of the existing images for each class ().

The first columns of tables 6 and 7 show the best performing visual descriptor for each remote sensing image class. For both LandUse and SceneSat datasets, the CNN-based descriptors are the best in the retrieval of all classes. SatResNet-50 performs better than other CNN architectures on most classes apart some classes containing objects rotated and translated on the image plane. In this case, NetVLAD demonstrated to perform better. Looking at Fig. 5 it is interesting to note that NetVLAD, which considers CNN features combined with local features, works better on object-based classes and more important that the SatResNet-50 network clearly outperforms the ResNet-50 thus demonstrating that the domain adaptation of the network to the remote sensing domain helped to handle with the heterogeneous content of remote sensing images.

In Fig. 6 the interpolated 11-points precision-recall curves achieved by a selection of visual descriptors are plotted. It is clear that, in this experiments CNN-based descriptors outperform again other descriptors. It is interesting to note that the SatResNet-50 network clearly outperforms the ResNet-50 thus confirming that the domain adaption has been very effective especially in the case of the SceneSat dataset. This is mostly due to the fact that both the AID and SceneSat datasets are made of pictures taken from Google Earth and then the image content is more similar. In contrast the LandUse dataset is made of picture taken from an aerial device and then the content is quite different in terms of resolution as already discussed in Section  3.3.1.

Concerning the computational cost, the Bag Dense SIFT (FV) is the most costly solution with the worst cost-benefit trade-off. Early after the Bag Dense SIFT (FV), the Vgg M is the other most costly descriptor that is about 200 more costly than the DT-CWT, that is among the global hand-crafted descriptors the best performing one.

One may prefer a less costly retrieval strategy that is less precise and then choose for the DT-CWT. Among the CNN-based descriptors, the Vgg M 128 has better values than the DT-CWT for both datasets. The Vgg M 128 is six times more costly than DT-CWT. Concluding, the Vgg M 128 descriptor has the best cost-benefit trade-off.

(a) (b)
Figure 7: Difference of performance (), when the Vgg M is employed, between the pseudo RF and the basic retrieval system, and between manual RF and the basic retrieval system. (a) LandUse. (b) SceneSat.
features ANMRR MAP P@5 P@10 P@50 P@100
Global hand-crafted
Hist. L 0.820 12.31 35.16 28.78 17.42 13.43
Hist. H V 0.783 15.94 53.16 42.82 23.03 16.62
Hist. RGB 0.789 15.37 51.41 41.22 21.89 16.10
Hist. rgb 0.804 14.16 48.09 37.01 20.19 14.93
Spatial Hist. RGB 0.824 13.87 35.15 27.60 16.96 13.27
Co-occ. matr. 0.863 8.56 19.02 16.68 11.88 9.87
CEDD 0.740 19.88 62.27 52.58 29.25 20.55
DT-CWT L 0.708 21.03 39.50 35.67 26.67 22.04
DT-CWT 0.677 24.59 55.13 48.30 32.24 25.00
Gist RGB 0.804 17.14 44.09 35.79 20.46 15.12
Gabor L 0.769 15.92 44.14 36.39 22.16 17.38
Gabor RGB 0.750 18.04 52.56 44.00 25.53 18.97
Opp. Gabor RGB 0.748 18.62 53.73 44.48 25.78 19.32
HoG 0.757 17.79 47.73 40.51 24.69 18.61
Granulometry 0.783 15.26 38.54 31.92 20.29 16.00
LBP L 0.762 16.82 52.23 44.32 26.25 18.61
LBP RGB 0.752 18.06 58.35 49.75 28.16 19.53
Local hand-crafted
Dense LBP RGB 0.747 19.09 59.49 50.99 28.76 20.17
SIFT 0.648 28.56 52.97 47.53 34.65 27.39
Dense SIFT 0.675 25.67 72.20 62.46 35.14 25.75
Dense SIFT (VLAD) 0.652 28.42 74.98 64.82 37.73 27.77
Dense SIFT (FV) 0.652 28.42 74.98 64.82 37.73 27.77
CNN-based
Vgg F 0.360 57.22 85.29 80.91 65.26 52.88
Vgg M 0.344 58.83 86.55 82.73 67.11 54.26
Vgg S 0.350 58.34 86.42 82.58 66.81 53.50
Vgg M 2048 0.348 58.27 85.63 82.21 67.21 53.82
Vgg M 1024 0.358 56.99 85.27 81.79 66.03 52.92
Vgg M 128 0.470 44.46 74.04 69.85 53.60 42.39
BVLC Ref 0.376 55.46 84.84 80.77 63.79 51.34
BVLC AlexNet 0.388 54.31 84.13 79.37 62.18 50.05
Vgg VeryDeep 16 0.365 56.30 84.25 79.88 64.36 52.50
Vgg VeryDeep 19 0.369 55.80 83.42 79.08 63.71 51.99
GoogleNet 0.293 63.89 97.49 90.85 72.81 58.51
ResNet-50 0.305 63.13 98.17 92.54 72.73 57.56
ResNet-101 0.301 63.28 98.43 92.47 72.22 57.83
ResNet-152 0.308 62.58 98.07 91.87 71.14 57.50
NetVLAD 0.324 61.03 97.61 90.30 70.53 56.21
SatResNet-50 0.185 76.55 98.86 95.48 83.79 69.95
(a) LandUse dataset
features ANMRR MAP P@5 P@10 P@50 P@100
Global hand-crafted
Hist. L 0.732 19.73 36.82 31.14 20.49 15.85
Hist. H V 0.713 23.04 42.77 35.80 22.36 16.56
Hist. RGB 0.725 21.34 40.84 33.99 20.85 16.17
Hist. rgb 0.716 22.62 42.73 35.91 22.08 16.43
Spatial Hist. RGB 0.742 21.55 36.92 30.25 19.77 15.14
Co-occ. matr. 0.825 11.57 21.00 17.64 12.72 10.85
CEDD 0.696 23.83 37.71 33.69 23.71 17.77
DT-CWT L 0.677 23.07 35.22 30.87 23.82 19.95
DT-CWT 0.586 33.01 50.65 45.09 32.54 24.23
Gist RGB 0.728 22.66 40.06 34.00 21.40 15.54
Gabor L 0.692 22.38 39.52 33.86 22.84 18.81
Gabor RGB 0.656 26.62 48.60 42.22 26.28 20.37
Opp. Gabor RGB 0.647 27.77 47.54 41.31 27.85 20.52
HoG 0.733 19.81 38.75 34.10 20.96 15.38
Granulometry 0.722 21.07 38.35 32.31 20.19 17.02
LBP L 0.701 22.12 46.43 38.80 23.09 17.50
LBP RGB 0.672 24.70 49.35 42.85 25.62 19.03
Local hand-crafted
Dense LBP RGB 0.669 24.31 50.43 42.40 25.72 19.31
SIFT 0.570 35.64 58.55 51.11 34.25 24.84
Dense SIFT 0.606 31.91 63.50 54.86 31.65 22.11
Dense SIFT (VLAD) 0.554 37.06 70.93 61.99 36.36 24.85
Dense SIFT (FV) 0.523 40.19 71.74 63.37 38.36 27.00
CNN-based
Vgg F 0.372 54.18 72.08 70.39 53.50 34.85
Vgg M 0.383 52.91 72.22 70.31 52.57 34.16
Vgg S 0.381 53.13 72.18 70.45 52.65 34.24
Vgg M 2048 0.386 52.38 71.72 69.83 52.21 34.02
Vgg M 1024 0.398 51.08 70.95 69.12 50.83 33.46
Vgg M 128 0.519 38.16 60.00 56.65 39.14 27.34
BVLC Ref 0.371 54.43 72.00 70.58 53.57 34.88
BVLC AlexNet 0.391 52.44 70.77 68.65 51.56 34.07
Vgg VeryDeep 16 0.402 50.55 71.44 69.18 50.26 33.28
Vgg VeryDeep 19 0.419 48.61 70.01 67.17 48.58 32.48
GoogleNet 0.299 62.12 85.87 81.74 58.14 39.42
ResNet-50 0.231 70.86 92.42 89.15 65.86 42.27
ResNet-101 0.248 68.61 91.88 88.15 63.92 41.50
ResNet-152 0.250 68.50 91.76 87.68 63.90 41.27
NetVLAD 0.332 59.19 86.25 81.34 55.74 37.08
SatResNet-50 0.027 96.14 99.10 98.74 93.25 51.39
(b) SceneSat dataset
Table 3: Results obtained with the Pseudo RF scheme with the Euclidean distance. The lower is the value of and the better is the performance. For the other metrics is the opposite. The best result is reported in bold.
features ANMRR MAP P@5 P@10 P@50 P@100
Global hand-crafted
Hist. L 0.798 15.02 71.40 48.34 21.13 15.24
Hist. H V 0.762 18.52 83.90 59.44 26.51 18.39
Hist. RGB 0.771 17.73 80.50 57.16 25.06 17.65
Hist. rgb 0.782 16.73 79.96 54.60 23.80 16.66
Spatial Hist. RGB 0.789 17.47 82.30 52.48 22.59 16.15
Co-occ. matr. 0.853 9.76 38.02 28.78 14.02 10.74
CEDD 0.722 22.23 87.97 66.76 32.28 22.04
DT-CWT L 0.691 23.19 65.54 50.27 29.25 23.32
DT-CWT 0.654 27.21 80.47 62.06 35.64 26.85
Gist RGB 0.767 20.40 84.43 57.66 26.08 18.12
Gabor L 0.754 17.99 71.10 51.05 24.59 18.57
Gabor RGB 0.734 20.11 77.10 56.96 28.26 20.32
Opp. Gabor RGB 0.732 20.80 80.10 58.71 28.55 20.73
HoG 0.733 20.49 78.10 57.23 28.59 20.52
Granulometry 0.770 17.28 67.09 47.36 22.82 17.10
LBP L 0.746 18.91 77.58 57.98 29.05 19.98
LBP RGB 0.737 20.08 82.13 62.40 30.86 20.80
Local hand-crafted
Dense LBP RGB 0.727 21.74 88.73 66.33 32.21 21.76
SIFT 0.602 32.89 88.78 67.16 40.66 31.04
Dense SIFT 0.649 28.38 93.43 75.42 38.82 27.86
Dense SIFT (VLAD) 0.623 31.18 94.79 77.60 41.74 30.13
Dense SIFT (FV) 0.623 31.18 94.79 77.60 41.74 30.13
CNN-based
Vgg F 0.329 60.53 97.39 89.99 69.47 55.64
Vgg M 0.316 61.85 97.82 91.13 70.93 56.64
Vgg S 0.320 61.45 97.58 90.98 70.77 56.04
Vgg M 2048 0.316 61.76 97.97 91.20 71.58 56.66
Vgg M 1024 0.326 60.55 97.78 90.83 70.48 55.73
Vgg M 128 0.422 49.53 95.30 83.89 59.85 46.46
BVLC Ref 0.347 58.63 97.50 89.95 67.71 53.91
BVLC AlexNet 0.357 57.63 97.26 88.90 66.53 52.72
Vgg VeryDeep 16 0.331 60.05 97.24 89.45 68.98 55.48
Vgg VeryDeep 19 0.334 59.56 96.97 88.88 68.39 54.92
GoogleNet 0.257 67.48 86.59 84.09 62.32 41.49
ResNet-50 0.181 77.11 93.53 92.07 71.44 44.65
ResNet-101 0.196 75.13 92.72 90.94 69.40 44.03
ResNet-152 0.203 74.62 92.64 90.63 69.39 43.49
NetVLAD 0.281 66.36 86.35 83.05 61.34 39.60
SatResNet-50 0.014 98.05 99.18 99.31 95.79 51.78
(a) LandUse dataset
features ANMRR MAP P@5 P@10 P@50 P@100
Global hand-crafted
Hist. L 0.698 24.02 67.76 47.75 23.30 17.22
Hist. H V 0.670 28.17 78.95 55.00 25.93 18.30
Hist. RGB 0.686 25.91 72.36 51.58 24.09 17.77
Hist. rgb 0.675 27.44 76.54 54.10 25.35 18.12
Spatial Hist. RGB 0.682 28.06 83.62 54.21 24.86 17.71
Co-occ. matr. 0.803 14.04 41.59 30.37 14.59 11.52
CEDD 0.653 29.59 75.30 54.08 27.26 19.60
DT-CWT L 0.648 26.88 62.55 45.02 26.21 21.13
DT-CWT 0.544 37.90 78.35 59.78 36.08 26.13
Gist RGB 0.660 29.42 83.10 56.85 27.20 18.60
Gabor L 0.663 26.39 68.52 48.85 25.20 19.86
Gabor RGB 0.622 31.02 77.15 57.26 29.28 21.74
Opp. Gabor RGB 0.610 32.29 76.50 57.07 30.94 22.03
HoG 0.688 24.93 72.06 52.61 24.88 17.15
Granulometry 0.701 24.55 67.02 46.41 22.07 17.63
LBP L 0.655 27.47 79.06 56.44 27.07 19.57
LBP RGB 0.628 29.79 78.91 58.92 29.37 20.93
Local hand-crafted
Dense LBP RGB 0.625 29.82 83.16 59.42 29.55 21.11
SIFT 0.508 42.25 89.27 68.54 39.56 27.79
Dense SIFT 0.554 37.83 90.55 71.12 36.35 24.38
Dense SIFT (VLAD) 0.493 43.48 94.37 77.44 41.69 27.70
Dense SIFT (FV) 0.493 43.48 94.37 77.44 41.69 27.70
CNN-based
Vgg F 0.330 59.44 92.22 80.82 57.44 36.75
Vgg M 0.340 58.30 92.36 81.06 56.41 36.10
Vgg S 0.336 58.69 92.42 81.15 56.74 36.34
Vgg M 2048 0.339 58.46 92.78 81.52 56.65 36.12
Vgg M 1024 0.349 57.21 92.78 80.85 55.31 35.73
Vgg M 128 0.448 46.57 90.89 74.01 45.39 30.70
BVLC Ref 0.327 59.89 92.38 81.55 57.63 36.80
BVLC AlexNet 0.343 58.26 92.72 80.74 55.97 36.16
Vgg VeryDeep 16 0.359 55.99 92.12 79.91 54.07 35.24
Vgg VeryDeep 19 0.368 54.79 91.84 79.23 53.23 34.76
GoogleNet 0.215 72.83 98.29 93.00 66.40 43.43
ResNet-50 0.153 80.54 99.76 97.28 74.27 45.92
ResNet-101 0.168 78.56 99.46 96.14 72.24 45.35
ResNet-152 0.173 78.30 99.62 96.43 72.43 44.82
NetVLAD 0.229 72.29 98.87 94.09 66.35 41.97
SatResNet-50 0.009 98.69 100.00 99.99 96.46 51.95
(b) SceneSat dataset
Table 4: Results obtained with the Manual RF scheme with the Euclidean distance. The lower is the value of and the better is the performance. For the other metrics is the opposite. For each row the best result is reported in bold.

4.2 Feature evaluation using the pseudo-RF retrieval scheme

In the case of pseudo RF, we used the top images retrieved after the initial query for re-querying the system. The computational cost of such a system is times higher than the cost of a basic system.

Results obtained choosing are showed in Table 3(a) and  3(b) for the LandUse and SatScene datasets respectively. It can be noticed that, in both cases, the employment of the pseudo RF scheme gives an improvement with respect to the basic retrieval system whatever is the visual descriptor employed. The CNN-based and local hand-crafted descriptors that, when used in a basic system, obtained the highest precision at level 5 (), have the largest improvement of performance.

Figures 7(a) and (b) show the difference of MAP between the pseudo RF scheme and the basic retrieval scheme, when the Vgg visual descriptor is employed. The value ranges from 0 (that corresponds to the basic system) to 10. It can be noticed that the improvement of performance, when is equal to 5, is of about in the case of LandUse and of about in the case of SceneSat dataset.

The second columns of tables 6 and 7 show the best performing visual descriptor for each remote sensing image class. In both cases, LandUse and SceneSat, the best performing visual descriptors are quite the same as in the case of the basic retrieval system.

4.3 Feature evaluation using the manual-RF retrieval scheme

In manual RF, we used the first actually relevant images retrieved after the initial query for re-querying the system. The computational cost of such a system is times higher than the cost of a basic system. The first five relevant images appear, in the worst case (co-occurrence matrix), within the top 50 images, while in the best case (SatResNet-50), within the top 6 or 7 images (cfr. table 1).

Results obtained choosing are showed in Table 4(a) and  4(b) for the LandUse and SatScene datasets respectively. It can be noticed that, in both cases, the employment of the manual RF scheme gives an improvement with respect to both the basic retrieval and the pseudo RF systems. The CNN-based and local hand-crafted descriptors that, when used in a basic system, obtained the highest precision at level 5 (), have also in this case the largest improvement of performance.

Figures 7(a) and (b) show the difference between the MAP of the manual RF scheme and the basic retrieval scheme, when the Vgg visual descriptor is employed, The value ranges from 0 (that corresponds to the basic system) to 10. It can be noticed that for both datasets the improvement of performance is, when is equal to 5, of about in the case of LandUse and of about in the case of SceneSat. The manual RF scheme, when is equal to 1, achieves the same performance of the pseudo RF when is equal to 2.

The third columns of tables 6 and 7 show the best performing visual descriptor for each remote sensing image class. In both cases, LandUse and SceneSat, the best performing visual descriptors are quite the same as in the cases of the basic and pseudo-RF retrieval system.

4.4 Feature evaluation using the active-learning-based-RF retrieval scheme

We considered the Active-Learning-based RF scheme as presented by Demir et al. Demir and Bruzzone (2015). As suggested by the original authors, we considered the following parameters: 10 RF iterations; an initial training set made of 2 relevant and 3 not relevant images; ambiguous images; diverse images; the histogram intersection as measure of similarity between feature vectors. The histogram intersection distance is defined as follows:

where and are the feature vectors of two generic images and is the size of the feature vector.

Results are showed in Table 5(a) and  5(b) for the LandUse and SatScene datasets respectively. Regarding the LandUse dataset, it can be noticed that the employment of this RF scheme gives an improvement with respect to the other retrieval schemes for all the visual descriptors. In the case of CNN-based descriptors the improvement is of about 20%. Surprisingly, in the case of SceneSat dataset, the employment of the Active-Learning-based RF scheme gives a performance improvement only in the cases of hand-crafted descriptors and most recent CNN architectures like ResNet or NetVLAD. In the best case, that is NetVLAD, the improvement is of about 80%. It is very interesting to note that for both datasets, the best performing descriptor is the NetVLAD. This is mostly due to the fact that this feature vector compared with the others extracted from different CNN architecture is less sparse. The degree of sparseness of feature vectors makes the Support Vector Machine, that is employed in the case of Active-Learning-based RF scheme, less or more effective.

The fourth columns of tables 6 and 7 show the best performing visual descriptor for each remote sensing image class. In the case of LandUse dataset, the best performing visual descriptors are the CNN-based descriptors, while in the case of SceneSat dataset, the best performing are the local hand-crafted descriptors apart from a few number of classes.

features ANMRR MAP P@5 P@10 P@50 P@100
Global hand-crafted
Hist. L 0.753 19.18 71.46 52.51 25.23 18.82
Hist. H V 0.688 25.57 87.31 70.88 34.65 24.31
Hist. RGB 0.747 20.12 80.07 61.73 27.77 19.50
Hist. rgb 0.712 23.25 83.35 63.96 30.50 22.22
Spatial Hist. RGB 0.695 25.27 84.49 64.50 32.39 23.52
Coocc. matr. 0.851 10.28 32.18 24.41 13.94 10.94
CEDD 0.719 22.72 85.49 68.70 32.43 21.94
DT-CWT L 0.757 18.78 56.10 42.78 24.67 18.81
DT-CWT 0.698 24.48 77.90 63.52 33.58 23.73
Gist RGB 0.662 27.76 87.06 70.17 37.91 27.11
Gabor L 0.748 19.19 61.67 44.56 23.95 18.61
Gabor RGB 0.709 23.43 69.60 53.30 30.05 22.74
Opp. Gabor RGB 0.634 29.78 82.34 66.71 39.40 29.42
HoG 0.681 26.18 84.70 68.10 36.06 25.20
Granulometry 0.833 14.39 54.11 36.23 16.92 12.76
LBP L 0.791 16.82 64.78 47.73 24.17 16.49
LBP RGB 0.793 16.77 69.43 51.66 24.26 16.41
Local hand-crafted
Dense LBP RGB 0.726 22.41 85.21 68.24 31.99 21.34
SIFT 0.572 35.40 80.31 65.13 44.08 34.64
Dense SIFT 0.631 32.09 90.27 76.93 41.81 29.82
Dense SIFT (VLAD) 0.598 34.39 88.87 76.44 43.70 31.92
Dense SIFT (FV) 0.465 48.38 98.58 92.97 61.48 44.70
CNN-based
Vgg F 0.256 69.33 99.70 97.47 79.92 63.90
Vgg M 0.247 71.09 99.39 98.09 82.98 65.54
Vgg S 0.260 69.45 99.34 96.42 79.88 63.61
Vgg M 2048 0.248 70.54 98.51 96.27 80.43 65.05
Vgg M 1024 0.266 68.22 99.09 96.84 78.75 62.73
Vgg M 128 0.333 60.72 97.70 94.03 73.60 56.17
BVLC Ref 0.292 66.05 98.70 95.98 76.56 60.72
BVLC AlexNet 0.281 66.98 99.49 96.77 77.36 61.58
Vgg VeryDeep 16 0.292 66.05 98.70 95.98 76.56 60.72
Vgg VeryDeep 19 0.292 66.05 98.70 95.98 76.56 60.72
GoogleNet 0.209 75.41 99.76 98.84 87.09 69.36
ResNet-50 0.332 63.34 99.45 98.01 77.00 56.72
ResNet-101 0.311 65.42 99.59 98.33 79.15 58.97
ResNet-152 0.318 64.74 99.68 98.38 78.71 57.81
NetVLAD 0.144 82.43 99.80 99.32 91.31 76.35
SatResNet-50 0.232 74.40 99.46 98.89 87.44 67.66
(a) LandUse dataset
features ANMRR MAP P@5 P@10 P@50 P@100
Global hand-crafted
Hist. L 0.648 28.93 67.96 52.19 27.10 20.01
Hist. H V 0.574 36.02 76.44 61.02 34.64 23.78
Hist. RGB 0.646 29.86 74.03 57.51 28.50 19.43
Hist. rgb 0.587 35.72 76.70 62.33 33.68 22.71
Spatial Hist. RGB 0.549 38.34 80.78 64.05 36.62 25.10
Coocc. matr. 0.804 14.72 42.03 31.14 14.93 11.25
CEDD 0.673 27.15 69.53 53.37 26.10 18.20
DT-CWT L 0.688 25.51 55.14 42.37 23.85 18.12
DT-CWT 0.571 37.60 71.54 57.35 35.32 23.91
Gist RGB 0.551 38.50 88.12 70.45 36.75 24.55
Gabor L 0.672 26.71 65.65 46.94 24.21 19.31
Gabor RGB 0.612 32.21 70.51 55.46 30.40 22.13
Opp. Gabor RGB 0.473 46.13 86.77 74.32 44.11 29.00
HoG 0.699 24.17 73.53 53.95 23.91 16.37
Granulometry 0.794 18.23 53.31 37.13 15.88 11.59
LBP L 0.669 26.62 72.44 54.86 26.68 18.24
LBP RGB 0.667 27.26 65.65 49.47 26.07 19.14
Local hand-crafted
Dense LBP RGB 0.534 39.84 84.20 68.96 37.18 26.25
SIFT 0.473 46.13 89.61 76.34 43.64 29.25
Dense SIFT 0.455 48.71 92.30 80.85 45.83 29.47
Dense SIFT (VLAD) 0.396 54.98 91.98 84.73 50.72 33.14
Dense SIFT (FV) 0.301 65.81 97.31 94.01 60.48 37.57
CNN-based
Vgg F 0.520 44.87 83.66 68.39 41.36 25.67
Vgg M 0.503 46.40 84.82 70.47 42.82 26.35
Vgg M 128 0.558 39.27 81.57 67.82 37.05 23.81
Vgg S 0.479 48.61 84.90 72.35 44.81 27.90
Vgg M 2048 0.467 48.85 84.60 72.33 46.05 28.59
Vgg M 1024 0.468 48.55 85.57 72.21 45.58 28.61
BVLC Ref 0.522 44.41 82.75 68.66 41.30 25.38
BVLC AlexNet 0.506 46.57 82.67 69.22 42.32 26.41
Vgg VeryDeep 16 0.505 46.77 86.97 72.49 42.40 26.27
Vgg VeryDeep 19 0.498 46.83 82.53 70.32 43.55 26.59
GoogleNet 0.112 86.56 99.84 99.34 81.44 46.96
ResNet-50 0.152 82.46 99.96 99.82 76.96 44.73
ResNet-101 0.162 81.52 99.96 99.64 75.86 44.16
ResNet-152 0.161 81.46 99.94 99.62 75.80 44.26
NetVLAD 0.048 93.89 100.00 99.94 89.11 50.28
SatResNet-50 0.054 94.20 99.98 99.98 91.42 49.29
(b) SceneSat dataset
Table 5: Results obtained with the Active-Learning-based RF scheme. The lower is the value of and the better is the performance. For the other metrics is the opposite. For each row the best result is reported in bold.
categories image basic IR pseudo RF manual RF act. learn. RF
agricultural Vgg M ResNet-101 Vgg VeryDeep 19 NetVLAD
(0.092) (0.065) (0.048) (0.011)
airplane SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.148) (0.103) (0.084) (0.033)
baseballdiamond SatResNet-50 SatResNet-50 SatResNet-50 BVLC Ref
(0.109) (0.060) (0.059) (0.076)
beach SatResNet-50 SatResNet-50 SatResNet-50 Vgg F
(0.031) (0.021) (0.021) (0.006)
buildings SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.412) (0.368) (0.302) (0.271)
chaparral NetVLAD NetVLAD NetVLAD NetVLAD
(0.007) (0.003) (0.003) (0.001)
denseresidential SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.564) (0.561) (0.521) (0.444)
forest SatResNet-50 Vgg M Vgg M NetVLAD
(0.035) (0.023) (0.021) (0.008)
freeway SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.280) (0.260) (0.256) (0.142)
golfcourse SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.232) (0.181) (0.169) (0.092)
harbor NetVLAD NetVLAD NetVLAD NetVLAD
(0.069) (0.051) (0.051) (0.001)
intersection SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.356) (0.289) (0.262) (0.212)
mediumresidential SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.390) (0.360) (0.337) (0.292)
mobilehomepark SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.329) (0.304) (0.278) (0.155)
overpass SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.190) (0.152) (0.130) (0.150)
parkinglot SatResNet-50 SatResNet-50 SatResNet-50 Vgg F
(0.002) (0.001) (0.001) (0.006)
river SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.365) (0.317) (0.283) (0.196)
runway GoogleNet GoogleNet GoogleNet NetVLAD
(0.256) (0.194) (0.191) (0.061)
sparseresidential SatResNet-50 SatResNet-50 SatResNet-50 GoogleNet
(0.194) (0.127) (0.105) (0.123)
storagetanks SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.363) (0.331) (0.278) (0.176)
tenniscourt SatResNet-50 SatResNet-50 SatResNet-50 GoogleNet
(0.402) (0.360) (0.275) (0.216)
Table 6: LandUse dataset: ANMRR evaluation across retrieval schemes and RS image classes . For each class and retrieval scheme is reported the best visual descriptor. Orange color stands for fine-tuned CNN-based descriptors, blue color stands for pre-trained CNN-based descriptors, yellow color stands for NetVLAD-based descriptors while cyan color stands for global hand-crafted descriptors.
categories image basic IR pseudo RF manual RF act. learn. RF
Airport SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.049) (0.015) (0.008) (0.015)
Beach SatResNet-50 SatResNet-50 Vgg M SatResNet-50
(0.046) (0.040) (0.037) (0.004)
Bridge SatResNet-50 SatResNet-50 SatResNet-50 GoogleNet
(0.021) (0.001) (0.001) (0.048)
Commercial SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.027) (0.008) (0.005) (0.084)
Desert SatResNet-50 SatResNet-50 SatResNet-50 Opp. Gabor RGB
(0.005) (0.003) (0.003) (0.004)
Farmland SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.054) (0.034) (0.013) (0.027)
footballField SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.001) (0.001) (0.001) (0.001)
Forest SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.017) (0.014) (0.005) (0.033)
Industrial SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.073) (0.050) (0.028) (0.145)
Meadow SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.020) (0.008) (0.006) (0.035)
Mountain SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.014) (0.005) (0.005) (0.003)
Park SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.030) (0.014) (0.008) (0.012)
Parking SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.003) (0.001) (0.001) (0.010)
Pond SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.014) (0.004) (0.003) (0.030)
Port SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.048) (0.016) (0.010) (0.058)
railwayStation SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.016) (0.021) (0.004) (0.031)
Residential SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.075) (0.034) (0.031) (0.033)
River SatResNet-50 SatResNet-50 SatResNet-50 SatResNet-50
(0.001) (0.001) (0.001) (0.005)
Viaduct SatResNet-50 SatResNet-50 SatResNet-50 NetVLAD
(0.001) (0.001) (0.001) (0.005)
Table 7: LandUse dataset: ANMRR evaluation across retrieval schemes and RS image classes . For each class and retrieval scheme is reported the best visual descriptor. Orange color stands for fine-tuned CNN-based descriptors, blue color stands for pre-trained CNN-based descriptors, yellow color stands for NetVLAD-based descriptors while cyan color stands for global hand-crafted descriptors.
basic IR pseudo RF manual RF act. learn. RF Overall
features ANMRR EQC ANMRR EQC ANMRR EQC ANMRR EQC avg rank
SatResNet-50 0.133 409 0.110 2045 0.097 2045 0.143 8180 5.08
GoogleNet 0.329 204 0.290 1020 0.254 1020 0.160 4080 5.49
ResNet-50 0.294 409 0.255 2045 0.229 2045 0.241 8180 5.72
ResNet-101 0.302 409 0.261 2045 0.234 2045 0.236 8180 5.92
ResNet-152 0.305 409 0.267 2045 0.240 2045 0.238 8180 6.59
Vgg M 2048 0.410 409 0.367 2045 0.327 2045 0.375 8180 8.87
NetVLAD 0.369 819 0.326 4095 0.277 4095 0.096 16380 9.02
Vgg M 1024 0.422 204 0.378 1020 0.337 1020 0.380 4080 9.19
Vgg M 0.398 819 0.363 4095 0.328 4095 0.386 16380 9.45
Vgg S 0.398 819 0.365 4095 0.328 4095 0.371 16380 9.55
Vgg F 0.397 819 0.366 4095 0.329 4095 0.398 16380 9.90
Vgg M 128 0.525 25 0.494 125 0.435 125 0.463 500 10.81
BVLC Ref 0.404 819 0.374 4095 0.337 4095 0.409 16380 11.18
DT-CWT 0.628 4 0.632 20 0.599 20 0.634 80 11.22
Vgg VeryDeep 19 0.427 819 0.394 4095 0.351 4095 0.307 16380 11.81
Vgg VeryDeep 16 0.416 819 0.383 4095 0.345 4095 0.403 16380 12.02
BVLC AlexNet 0.416 819 0.389 4095 0.350 4095 0.403 16380 12.03
SIFT 0.597 204 0.609 1020 0.555 1020 0.522 4080 12.45
Dense SIFT 0.638 204 0.641 1020 0.602 1020 0.543 4080 12.61
Opp. Gabor RGB 0.692 52 0.698 260 0.671 260 0.553 1040 13.56
DT-CWT L 0.690 1 0.693 5 0.670 5 0.655 20 13.74
Dense SIFT (FV) 0.579 8192 0.582 40960 0.535 40960 0.498 163840 13.82
Dense SIFT (VLAD) 0.601 5120 0.603 25600 0.559 25600 0.384 102400 13.93
Dense LBP RGB 0.702 204 0.708 1020 0.676 1020 0.631 4080 14.10
Gabor RGB 0.699 19 0.703 95 0.678 95 0.659 380 14.27
LBP RGB 0.708 10 0.713 50 0.683 50 0.730 200 14.82
CEDD 0.710 28 0.718 140 0.687 140 0.696 560 14.95
Hist. H V 0.741 102 0.747 510 0.715 510 0.630 2040 15.55
Gabor L 0.726 6 0.731 30 0.709 30 0.709 120 15.76
LBP L 0.725 48 0.732 240 0.701 240 0.729 960 16.01
Gist RGB 0.743 102 0.765 510 0.713 510 0.606 2040 16.53
HoG 0.737 16 0.744 80 0.710 80 0.688 320 16.91
Hist. rgb 0.750 153 0.759 765 0.728 765 0.648 3060 17.18
Granulometry 0.748 15 0.752 75 0.735 75 0.813 300 17.39
Hist. L 0.772 51 0.776 255 0.748 255 0.661 1020 18.11
Hist. RGB 0.754 153 0.757 765 0.728 765 0.695 3060 18.35
Spatial Hist. RGB 0.763 307 0.783 1535 0.735 1535 0.621 6140 18.67
Coocc. matr. 0.841 1 0.844 5 0.828 5 0.827 20 19.08
Table 8: Average rank across datasets of each visual descriptor performance. The list of visual descriptors reported in the table is ordered by the average rank (last column of the table) that is obtained by averaging the ranks achieved by each visual descriptor across datasets, retrieval schemes and measures: , , at 5,10,50,100 levels, and EQC. For sake of completeness, for each retrieval scheme, the table shows the average ANMRR across datasets and the EQC for each visual descriptor. For each retrieval scheme, the best average ANMRR performance is reported in bold.

4.5 Average rank of visual descriptors across RS datasets

In table 8 we show the average rank of all the visual descriptors evaluated. The average rank is represented in the last column and obtained by averaging the ranks achieved by each visual descriptor across datasets, retrieval schemes and measures: , , at 5,10,50,100 levels, and EQC. For sake of completeness, for each retrieval scheme, we displayed the average ANMRR across datasets and the EQC for each visual descriptor. From this table is quite clear that across datasets, the best performing visual descriptors are the CNN-based ones. The first 13 positions out of 38 are occupied by CNN-based descriptors. The global hand-crafted descriptor DT-CWT is at 14th position mostly because of the length of the vector that is very short. After some other CNN-based descriptors, we find the local hand-crafted descriptors that despite their good performance, they are penalized by the size of the vector of feature that is very long, in the case of Dense SIFT (FV) is 40960 that is 2048 times higher than the size of DT-CWT.

Looking at the EQC columns of each retrieval schemes of table 8, it is quite evident that the use of Active-Learning-based RF is not always convenient. For instance, in the case of the top 5 visual descriptors of the table, the Active-Learning-based RF achieves globally worse performance than pseudo-RF with a much more higher EQC. This is not true in all other cases, where the performance achieved with the Active-Learning-based RF is better than pseudo-RF.

Notwithstanding this, the employment of techniques to speed-up the nearest image search process makes the AL-RF scheme not as computationally expensive as argued in the previous paragraph. Large amount of data and high dimensional feature vector, makes the nearest image search process very slow. The main bottleneck of the search is the access to the memory. The employment of a compact representation of the feature vectors, such as hash Zhao et al. (2015) or polysemous codes Douze, Jégou, and Perronnin (2016), is likely to offer a better efficiency than the use of full vectors thus accelerating the image search process. Readers who would wish to deepen the subject can refer to the following papers Zhao et al. (2015); Lu, Liong, and Zhou (2017); Douze, Jégou, and Perronnin (2016); Zhao et al. (2015).

4.6 Comparison with the state of the art

According to our results, one of the best performing visual descriptor is the ResNet and in particular SatResNet-50, while the best visual descriptor, when the computational cost is taken into account, is the Vgg M 128. We compared these descriptors, coupled with the four scheme described in Sec. 3.2, with some recent methods Bosilj et al. (2016); Aptoula (2014); Ozkan et al. (2014); Yang and Newsam (2013). All these works used the basic retrieval scheme and the experiments have been conducted on the LandUse dataset. Aptoula proposed several global morphological texture descriptors Bosilj et al. (2016); Aptoula (2014). Ozkan et al. used bag of visual words (BoVW) descriptors, the vector of locally aggregated descriptors (VLAD) and the quantized VLAD (VLAD-PQ) descriptors Ozkan et al. (2014). Yang et al. Yang and Newsam (2013) investigated the effects of a number of design parameters on the BoVW representation. They considered: saliency-versus grid-based local feature extraction, the size of the visual codebook, the clustering algorithm used to create the codebook, and the dissimilarity measure used to compare the BOVW representations.

The results of the comparison are shown in Table 9. The Bag of Dense SIFT (VLAD) presented in Ozkan et al. (2014) achieves performance that is close to the CNN-based descriptors. This method achieves with . This result has been obtained considering a codebook built by using images from the LandUse dataset. Concerning the computational cost, the texture features Yang and Newsam (2013); Aptoula (2014) are better than SatResNet-50 and Vgg M 128. In terms of trade-off between performance and computational cost, the Vgg M 128 descriptor achieves a value that is about 25% lower than the one achieved by the CCH+RIT+FPS+FPS descriptor used in Aptoula (2014) with a computational cost that is about 2 times higher.

features Hist. Inters. Euclidean Cosine Manhattan -square Length Time (sec) EQC
CCH RIT FPS FPS Aptoula (2014) 0.609 0.640 - 0.589 0.575 62 - 12
CCH Aptoula (2014) 0.677 0.726 - 0.677 0.649 20 1.9 4
RIT Aptoula (2014) 0.751 0.769 - 0.751 0.757 20 2.3 4
FPS Aptoula (2014) 0.798 0.731 - 0.740 0.726 14 1.6 2
FPS Aptoula (2014) 0.853 0.805 - 0.790 0.783 8 1.6 1
pLPS-aug Bosilj et al. (2016) - 0.472 - - - 12288 - 2458
Texture Yang and Newsam (2013) - 0.630 - - - - 40.4 -
Local features Yang and Newsam (2013) 0.591 - - - - 193.3 -
Dense SIFT (BoVW) Ozkan et al. (2014) - - 0.540 - - 1024 9.4 204
Dense SIFT (VLAD) Ozkan et al. (2014) - - 0.460 - - 25600 129.3 5120
B-IR Vgg M 128 0.544 0.488 0.488 0.493 0.488 128 - 25
B-IR ResNet-50 0.476 0.358 0.358 0.395 0.350 2048 - 409
B-IR SatResNet-50 0.331 0.239 0.239 0.271 0.233 2048 - 409
P-RF Vgg M 128 0.550 0.470 0.470 0.466 0.458 128 - 125
P-RF ResNet-50 0.493 0.305 0.305 0.390 0.324 2048 - 2045
P-RF SatResNet-50 0.332 0.185 0.185 0.250 0.200 2048 - 2045
M-RF Vgg M 128 0.497 0.422 0.422 0.416 0.410 128 - 125
M-RF ResNet-50 0.459 0.181 0.181 0.359 0.299 2048 - 2045
M-RF SatResNet-50 0.307 0.014 0.014 0.224 0.179 2048 - 2045
AL-RF Vgg M 128 0.333 - - - - 128 - 500
AL-RF ResNet-50 0.332 - - - - 2048 - 8180
AL-RF SatResNet-50 0.232 - - - - 2048 - 8180
Table 9: ANMRR comparison on the LandUse dataset. The lower is the result, the better is the performance. For each column the best result is reported in bold.

5 Conclusions

In this work we presented an extensive evaluation of visual descriptors for content-based retrieval of remote sensing images. We evaluated global hand-crafted, local hand-crafted and Convolutional Neural Networks features coupled with four different content-based retrieval (CBIR) schemes: a basic CBIR, a pseudo relevance feedback (RF), a manual RF and an active-learning-based RF. The experimentation has been conducted on two publicly available datasets that are different in terms of image size and resolution. Results demonstrated that:

  • CNN-based descriptors proved to perform better, on average, than both global hand-crafted and local hand-crafted descriptors whatever is the retrieval scheme adopted and on both the datasets considered, see the summary table 8;

  • The RS domain adaptation of the ResNet-50 has led to a notable improvement of performance with respect to CNNs pre-trained on multimedia scene and object images. This demonstrated the importance of domain adaptation in the field of remote sensing images;

  • NetVLAD works better on those images that contain fine-grained textures and objects. NetVLAD is a CNN that considers local features. This is true specially for the LandUse dataset on classes like: chaparral, harbor, runaway, etc. See the tables 6 and 7 and figures 5;

  • Pseudo and manual relevance feedback schemes demonstrated to be very effective only when coupled with a visual descriptor that is high performing in a basic retrieval system, such as CNN-based and local hand-crafted descriptors. This is quite evident looking at 7a and b;

  • Active-Learning-based RF demonstrated to be very effective on average and the best performing among retrieval schemes. The computational cost required to perform one query is, on average, at least 4 times higher than the computational cost required to perform a query with the other considered RF schemes and at least 20 times higher than a basic retrieval scheme.

As future works, it would be interesting to experiments the efficiency of techniques to speed up the image search process by exploiting compact feature vector representations such as has, or polysemous codes.

Competing interests

The author declare that he has no competing interests.

Funding

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for doing part of the experiments included in this research.

Acknowledgments

The author is grateful to Prof. Raimondo Schettini for the valuable comments and stimulating discussions and he would like to thank the reviewers for their valuable comments and effort to improve the manuscript.

Footnotes

  1. http://www.ivl.disco.unimib.it/activities/cbir-rs/
  2. http://vision.ucmerced.edu/datasets
  3. http://dsp.whu.edu.cn/cn/staff/yw/HRSscene.html

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