# Inquisition of Vision Transformer for Content Based Satellite Image Retrieval

S. Dhruv Shindhe, A. Faheema

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## To cite this version:

S. Dhruv Shindhe, A. Faheema. Inquisition of Vision Transformer for Content Based Satellite Image Retrieval. 7th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Jan 2023, Chennai, India. pp.171-182, 10.1007/978-3-031-39811-7_14. hal-05363234

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# Inquisition of Vision Transformer for Content Based Satellite Image Retrieval

S. Dhruv Shindhe¹ and A. G. J. Faheema²(✉)

¹ ARTPARK, Indian Institute of Science, Bangalore, India
dhruv@artpark.in

² Centre for AI & Robotics, Bangalore, India
faheema.cair@gov.in

**Abstract.** Content based satellite image retrieval is among the most vital technologies for military application. Huge volumes of satellite imagery get generated day by day. Management of huge archives of satellite image requires intelligent image retrieval system. In this paper, we have carried out study using SOTA Vision Transformer and other deep CNN based features in conjunction with various matching and indexing methods. We conducted exhaustive experiments by employing various deep features and different variants of Vision Transformer with variety of feature vector matching techniques. Experimental results conducted on various benchmark satellite image retrieval dataset demonstrate the promising capability of the Vision Transformer in conjunction with Ball tree. ResNet & DenseNet have outperformed on two benchmark datasets namely RS19, & PatternNet. ViT has outperformed on RSSC7N dataset & has performed on par with ResNet & DenseNet on UCM dataset.

**Keywords:** ResNet · DenseNet · ViT · Ball tree · BERT

## 1 Introduction

The amount of satellite images getting generated is increasing day by day. Intelligent image retrieval aims at retrieval of semantically relevant images from massive image archive quickly. Content Based Image Retrieval (CBIR) is pretty popular. Despite the fact that the region has seen a lot of activity, CBIR for EO/IR images, not much work is reported in literature using satellite images. CBIR using satellite images is very challenging task due to high resolution images. CBIR system comprises of module for feature extraction, matching and retrieval. A quit a lot of work is got into feature extraction, feature selection, feature indexing and feature vector matching. Feature extraction aims at extraction of feature vectors from images. Latency is one of critical factor in CBIR systems. Most of traditional methods of feature extraction focuses of representing images using low level, mid and high-level handcrafted features. Recently, due to revolution in computing image data due to huge success of deep learning, a lot of researchers are focusing on development of CBIR using satellite images. The main challenge in development
of content based image retrieval systems; 1) handling high resolution satellite images, (2) accurately characterize satellite image with relevant features and, (3) quickly retrieve semantically similar images from large archive of images. Feature extraction plays a key role in representing satellite images. Feature extraction is dealt with mostly global features for characterizing the entire image. Global features vectors are used to represent the entire image capturing color, texture & shape features.

Later with advent of SIFT (Scale Invariant Feature Transform), researchers started using local features for representing images. There was a lot of research which focused on employing combination of local and global features for image characterization. After 2012, researchers started employing deep features for image retrieval. VGGNet, ResNet, DenseNet, EfficientNet & MobileNet were among popular architectures which are widely used for image characterization by tapping the features from N-1 layer. Models already trained on ImageNet were used to initialize the network and transfer learning is carried out for specific set of images. Recently, Vision Transformer (ViT) has reported huge success for tasks like image classification, object detection and segmentation. In this paper, we implemented ViT for feature extraction using satellite image.

Development of CBIR using satellite images is very challenging. In order to handle the high-resolution satellite images, the images are divided into tiles. Features are extracted from the tiles. We have analyzed the usage of deep CNN and ViT features for satellite image characterization. In order to carry out fast indexing, matching and retrieval we have used Ball tree data structure. Experimental section discuss in detail various experiments using deep CNN and ViT with various similarity metric for benchmark dataset as discussed in section dataset details. To the best of our knowledge this is first attempt which employs ViT & Ball tree for satellite image retrieval, which handles the main challenges in CBIR. Combination of ViT and ball tree-based matching & indexing demonstrates excellent performance of ViT.

Therefore, in this paper, we make use of deep CNN based deep feature and ViT features for CBIR using satellite images. The proposed feature extraction in conjunction with ball tree data structure tends to be fast, accurate and precise for retrieval of semantically relevant satellite images from large archive of images.

The paper is structured as below: the next section, Sect. 2 describes and discusses the related work. Section 3 describes various features extraction method. Section 4 provides details about experimental environment, dataset, evaluation metrics, and discussion. Finally, Sect. 5 captures the conclusion drawn and future work.

## 2 Related Work

CBIR aims at retrieval of similar images from a huge archive of images stored in databases. Earlier researchers used low level, mid level and high level global features to represent the images. In tradition methods researchers used handcrafted spectral features for satellite image characterization. The authors of [1, 2] used the reflectance of the surface of the earth to encode the images. Texture features were also used for satellite image retrieval; they represent a structure in a set of pixels. Haralick et al. [3] used gray level co-occurrence matrices for image retrieval. Ma et al. [4] used shape feature for the image retrieval task. Shape characteristics define the contour or area of ground objects, but they
have limited ability to capture information about their spatial relationships. With advent
of SIFT (Scale Invariant Feature Transform) researcher started representing images using
local features. Aptoula et al. [5] used Bag-of-Visual-Word(Bow) encoding for satellite
image retrieval. The BoW approach uses visual codebook, to generate aggregate feature
vector. Due to evolution of deep learning in the year 2012, researchers started using
deep features for characterization of satellite images. Napole-tano et al. [6] showed that,
regardless of the retrieval strategy used, CNN based features outperform both global
and local hand-crafted features. Imbriaco et al. [7] implemented a pipeline that uses
the Vector of Locally Aggregated Descriptors to combine attentive for image retrieval,
local convolutional features into a global descriptor (VLAD). They demonstrated that
the local convolutional features and global representation outperform other systems even
in the absence of training. Kavitha et al. [8] proposed image retrieval which used Fuzzy
clustering and fuzzy logic-based fuzzy multi-characteristic clustering approach. In a
sizable database of remote sensing pictures, Demir et la. [9] demonstrated hash based
image search and retrieval. Pair of kernel based non-linear hashing algorithms were
used for characterization of remote sensing images. The effectiveness of hashing tech-
niques was evaluated in terms of remote sensing picture retrieval precision and time.
Sebai et al. [10] proposed descriptors for high resolution satellite image retrieval using
SIFT (Scale Invariant Features) and SVM linear classification. They had implemented
image augmentation to improve the performance of the classifier. Tsay et al. [11] used
an auto encoder for the extraction of cloud and precipitation features for satellite rain-
fall retrieval. In most of the work low level, mid-level and deep learning based features
have been implemented for satellite image retrieval systems, but no work has yet been
done using ViT. In this paper, we have tried to evaluate deep CNN and ViT features for
characterization of satellite image for image retrieval task.

3 Deep and Vision Transformer Based Feature Extraction

Image retrieval based on content comprises of two stages, offline stage and online stage.
In the offline stage, given an archive of satellite images, each satellite image is represented
by deep or ViT feature vector to create feature matrix. Deep CNN or ViT feature vector
characterize the image globally. In the online stage, given an image dataset and its
corresponding feature matrix and feature extraction type, feature vector is extracted for
the query images. The query feature vector is matched with database feature vector
to retrieve similar images. In this paper our main focus is on feature extraction and
feature matching techniques. In the following subsection various deep and ViT feature
extraction methods are briefly described. We have implemented VGG16 [14], DenseNet-
121 [15] and RestNet50 [16] and 3 different variants of ViT for feature extraction. For
both deep learning based & Vision Transformer architecture, pre-trained models trained
on ImageNet are used.

## 3.1 Feature Extraction

In order to develop fast and accurate CBIR system for satellite images, strong discrimi-
native features are essential. In the following section various feature extraction methods
are described in detail.
### 3.1.1 Deep Features

Transfer learning is leveraged to extract deep features from pre-trained model. We have only the chosen top performing deep architectures which include VGGNet, DenseNet, ResNet and ViT.

#### • VGG

VGG architecture is an improvement over AlexNet which replaces larger kernels with a smaller 3x3 kernels in succession. The VGG16 model is made up of five convolution blocks, each of which has two convolution layers (size 3X3) and one max pooling layer (size 2X2). It consist of 16 layers of which three are fully connected layers, we use a pre-trained VGG16 model to initialize the network. Feature vectors are tapped from the fully connected layer 1. The feature vector size 4096 dimensions.

#### • Residual Networks (ResNet)

Incorporated the use of residual blocks to address the vanishing gradient issue. This method introduces the technique of skip connections.

So, instead of say $H(x)$, initial mapping, let the network fit, $F(x) = H(x) - x$ which gives $H(x) = F(x) + x$. (1).

To extract features from ResNet models, we imported the pretrained ResNet models from Pytorch. After importing the models, we replaced *model.fc* with *nn.Identity*. Identity() will just return the input without any clone usage or manipulation of the input and since the features are its input, the output of the entire model will be the features. We have leveraged pre-trained models of various variants of ResNet model such as ResNet-18, ResNet-34, ResNet-50, ResNet-101 and ResNet-152 for extracting deep features for our experiments.

#### • DenseNet

Each layer in DenseNet receives inputs from all preceding levels, implying that each layer has access to the cumulative knowledge of all preceding layers. They reduce the number of parameters, enhance feature propagation, encourage feature reuse, and address the vanishing-gradient problem. DenseNet has better accuracies over SOTA methods and requires less memory and computation to attain good performance. To extract features from DenseNet models, we imported the pre-trained DenseNet models from Pytorch. After importing the models, we replaced *model.classifier* with *nn.Identity*. The size of feature vector is 1024.

#### • Vision Image Transformers (ViT)

A model for image retrieval called the Vision Transformer, or ViT, applies a Transformer-like design to selected portions of the image. Vision Transformers do not use convolutional layers and instead apply a transformer to picture patches, resulting in excellent performance on various computer vision tasks. ViT achieves great performance as compared to SOTA convolutional networks. In ViT an image is divided into small patches and flattened into a vector of a certain size, consequently inputted to a linear projection
layer which outputs a patch embedding, these along with the positional embedding helps retain the position of the image patches, these are simultaneously fed onto a transformer, and a Multi-Layer Perceptron (MLP) on the head on the transformer performs the classification. ViT uses self-attention by using multiple head unlike CNN's their average attention distance varies where some attend to too much information while some pay attention to the small regions, unlike the receptive fields in a CNN which learn hierarchy of image features. Transformers lack inductive biases. Different variants of ViT are shown in Table 1. ViT-B/16 denotes the Base version of BERT (Bi-directional Encoder Representation from Transformer) with 16x16 input image patches. The features are tapped from N-1 layer, which in the ViT is called as lambda layer. The feature vector size of ViT-B/16 is 768 & 1024 for ViT-L/16 and ViT-L/32. Again, using the similar approach, we extracted the feature vectors from ViT models. The pretrained ViT models which were leveraged are: Vit_b_16, Vit_b_32, Vit_l_16 and Vit_l_32 (Fig. 1).

<table><caption>**Table 1.** Variants of Vision Transformer [20]</caption><thead><tr><th>Model</th><th>Layers</th><th>Hidden layer size</th><th>MLP v size</th><th>Head</th><th>Param</th></tr></thead><tbody><tr><td>ViT-Base</td><td>12</td><td>768</td><td>3072</td><td>12</td><td>86 M</td></tr><tr><td>ViT-Large</td><td>24</td><td>1024</td><td>4096</td><td>16</td><td>307 M</td></tr><tr><td>ViT-Huge</td><td>32</td><td>1280</td><td>5120</td><td>16</td><td>632 M</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Overview of the ViT architecture [20]</figcaption></figure>

## 3.2 Matching and Retrieval

The matching stage involves comparing the query image feature vector to the database feature vectors. A variety of methods such as L1, L2, Mahalanobis, KD trees, local
sensitive hashing, and ball tree matching evaluated for image retrieval. We carried out
thorough analysis of various methods and found out Ball tree matching found to be very
fast and accurate in retrieving similar satellite images. Data is divided into a series of
nesting hyper spheres using ball trees. As a result, creating a tree is more expensive than
creating a KD tree, but the resulting data structure can be far more effective with well-
organized data, especially in high-dimensional space. KD tree is an approximate nearest
neighbor algorithm and it does not work well for high dimensional data due to curse of
dimensionality. Time complexity of Balls tree is O(D log N), where D is the dimension
of data and N the number of samples. Ball trees are binary trees, it starts off with two
hypersphere or n dimensional clusters, a given test point can only belong to one cluster,
the one which it is closest to. If a situation arises where one point is equidistant from
two clusters it is randomly assigned one cluster. With each node's point falling into the
hyper-sphere which is given by r and C, a ball tree iteratively creates nodes from inputs.
The hypersphere of the test point is calculated by calculating the Euclidean distance
between the test point and the centroid of all the hypersphere. Due to their spherical
geometry they outperform KD trees.

## 3.3 Dataset Details

RS19, RSSCN7, UCM & PatternNet are benchmark dataset for high resolution satellite image retrieval tasks. RS19 also known as WHU-RS19 [17] collection is made up of satellite pictures downloaded from Google Earth, which offers high-resolution satellite photographs up to 0.5 m. It has 19 different image categories, with around 50 images of size 600 * 600 in each category resulting in total of 1005 images. This dataset has 19 different categories.

The RSSCN7 [18] is another remote sensing image dataset. This dataset is generated by Wuhan University. These pictures depict seven various settings, including a farm, a parking lot, a residential area, an industrial location, and a lake. There are 400 400 × 400 pixel photos in each category. This dataset is quite challenging because of the variety of scenarios. There are 2800 photos altogether in this dataset.

The UCM [13] dataset comprises of 21 scene type images such as airplane, beaches, building and dense residential areas. This comes from U.S Geological city map. This dataset has 100 images of size 256 × 256 and with spatial resolution of each pixel as 0.3 m in each category resulting in total of 2100 images.

PatternNet [19] dataset was generated from high resolution satellite images. It con-
tains 800 images per 38 various class. PatternNet is large scale dataset generated to make
it suitable deep learning based satellite image retrieval task.

## 3.4 Evaluation Metric

Calculations of precision, recall, and standard mean average precision make up the widely used metrics for assessing retrieval systems (mAP). Precision is the ratio of the number of relevant photographs found to the total number of images found. Recall is the proportion of relevant photos that were found among all relevant images. Here is how precision and recall are calculated:

$$ 
Precision = TP/(TP + FP) \qquad (1)
$$
$$Recall = TP(TP + FN) \qquad (2)$$

$$mAP = \sum_{j=1}^{J} AvgP_{j} \qquad (3)$$

TP, FP, TN, FN, and TR stand for True Positive, False Positive, True Negative, and False Negative.

Where AvgP is

$$AvgP = \sum_{k=1}^{N} P(k)rel(k) \text{ NoR} \qquad (4)$$

where P(k) is the accuracy rate, rel(k) is a piecewise function, and N is the total number of images in the dataset. Number of Relevant Images, or NoR For each image returned, the precision is calculated once and multiplied by the coefficient rel (k). The rel(k) is 1 if the current returned image is related; otherwise, it is 0.

## 3.5 Implementation Details

The experiments were conducted using pytorch open source framework. The experimental platform uses High end workstation 64 GB memory & 4TB hard disk with NVIDIA GeForce RTX 1080 Ti graphics card. Pre-trained model for deep models & vision transformer are used for feature extraction from satellite images. The algorithms are implemented using pytorch, opencv and scikit-learn libraries.

# 4 Experiments Results

We conducted comprehensive experiments to assess the effectiveness of deep CNN and ViT features with a range of distance metrics for content-based satellite image retrieval. We used four benchmark high resolution remote sensing image datasets, RS19, RSSCN7, UCM, and PatternNet, as mentioned in dataset details, to assess the effectiveness of the satellite image retrieval utilising deep features & ViT approach.

## 4.1 Results

In order to carry out qualitative analysis of our CBIR system using satellite images, we used top performing ResNet and ViT in conjunction with Ball tree data structure. Figure 2 (a–d) demonstrate the visual results obtained using images from benchmark dataset. From the visual results it is evident that our method is able to retrieve semantically similar images. From the Fig. 2(a–d) shows the retrieval results for various queries along with the distance metrics using ViT.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a–d) Visual results of CBIR using to performing model using Ball tree. Query image and top matching retrieved images are shown in above images</figcaption></figure>

## 4.2 Precision Recall Plots

We have also evaluated our CBIR system using well known precision recall curve. Figure 3(a-d) depicts the precision recall plots for RS19, RSSCN7, UCM and Pattern-Net dataset respectively. It is listed in Table 2, 3 and 4. We can infer that ResNet-50 and DenseNet-121 have outperformed ViT's by a good margin for RS19 dataset. ViT's on the other hand have had good precision on categories like beach, desert and viaduct. From Table 3 we can infer that ViT-L/32 outperforms all models with a good margin for RSSCN7 dataset. ViT's also have achieved a very good average precision on categories like Resident and forest. From Table 4, we can infer that ResNet-50 has the highest average precision for UCM dataset and ViT-B/16 comes very close to ResNet-50. From Table 5, it is clear that DenseNet-121 has outperformed all the network followed by ResNet for PatternNet dataset. ViT-B/16, ViT-L/16 are comparable with VGGNet. In order to further demonstrate the effectiveness of image retrieval we computed precision & recall for all the datasets. Figure 3(a) to 3(d) demonstrates accuracy and memory for the top 40 results in each class. In order to get the results, 40 search photos from each category were chosen, and the results were then averaged. Figure 3(a), 3(b), 3(c) and 3(d) depicts the precision vs recall for RS19, RSSCN7, UCM and PatternNet respectively. The findings from precision recall curves agrees with the analysis listed in Table 2, 3, 4 and Table 5.
<figure><img src="image_6.png" /><figcaption>Fig. 3. (a–d): Precision vs Recall plots for various benchmark satellite image dataset</figcaption></figure>

<table><caption>**Table 2.** Average Precision vs Recall for RS19 dataset</caption><thead><tr><th>Query size</th><th>VGG</th><th>Dense-Net121</th><th>Res-Net50</th><th>ViT-B/16</th><th>ViT-L/16</th><th>ViT-L/32</th></tr></thead><tbody><tr><td>10</td><td>0.78</td><td>0.86</td><td><strong>0.88</strong></td><td>0.78</td><td>0.76</td><td>0.80</td></tr><tr><td>20</td><td>0.72</td><td>0.79</td><td><strong>0.82</strong></td><td>0.70</td><td>0.68</td><td>0.73</td></tr><tr><td>30</td><td>0.65</td><td>0.75</td><td><strong>0.78</strong></td><td>0.63</td><td>0.63</td><td>0.67</td></tr><tr><td>40</td><td>0.58</td><td>0.71</td><td><strong>0.73</strong></td><td>0.57</td><td>0.59</td><td>0.61</td></tr></tbody></table>

## 4.3 Time

We also conducted quantitative experiment to determine the time taken by various Vision Transformer variants by using L1, Cosine distance & Ball tree based matching techniques. Figures 4(a–c) clearly shows superior performance of Ball tree as compared to L2 & Cosine distance. We can conclude from above analysis ViT with ball tree-based matching is an effective solution for satellite image retrieval.

We can infer from Fig. 4(a–c) it advisable to use Ball tree in conjunction with ViT for superior performance. We also feel if we train the ViT, we will be able to achieve SOTA performance.
<table><caption>**Table 3.** Average Precision vs Recall for RSSCN7 dataset</caption><thead><tr><th>Query size</th><th>VGG</th><th>Dense-Net121</th><th>Res-Net50</th><th>ViT-B/16</th><th>ViT-L/16</th><th>ViT-L/32</th></tr></thead><tbody><tr><td>10</td><td>0.74</td><td>0.84</td><td>0.80</td><td><strong>0.83</strong></td><td>0.76</td><td><strong>0.83</strong></td></tr><tr><td>20</td><td>0.69</td><td>0.74</td><td>0.75</td><td>0.77</td><td>0.72</td><td><strong>0.80</strong></td></tr><tr><td>30</td><td>0.64</td><td>0.69</td><td>0.69</td><td>0.71</td><td>0.68</td><td><strong>0.76</strong></td></tr><tr><td>40</td><td>0.63</td><td>0.64</td><td>0.64</td><td>0.65</td><td>0.66</td><td><strong>0.73</strong></td></tr></tbody></table>

<table><caption>**Table 4.** Average Precision vs Recall for UCM dataset</caption><thead><tr><th>Query size</th><th>VGG</th><th>Dense-Net121</th><th>Res-Net50</th><th>ViT-B/16</th><th>ViT-L/16</th><th>ViT-L/32</th></tr></thead><tbody><tr><td>10</td><td>0.83</td><td><strong>0.88</strong></td><td>0.87</td><td>0.87</td><td>0.83</td><td>0.82</td></tr><tr><td>20</td><td>0.79</td><td>0.81</td><td><strong>0.83</strong></td><td>0.80</td><td>0.72</td><td>0.74</td></tr><tr><td>30</td><td>0.77</td><td>0.74</td><td><strong>0.77</strong></td><td>0.75</td><td>0.67</td><td>0.67</td></tr><tr><td>40</td><td>0.67</td><td>0.70</td><td><strong>0.72</strong></td><td>0.70</td><td>0.62</td><td>0.64</td></tr></tbody></table>

<table><caption>**Table 5.** Average Precision vs Recall for Pattern Net dataset</caption><thead><tr><th>Query size</th><th>VGG</th><th>Dense-Net121</th><th>Res-Net50</th><th>ViT-B/16</th><th>ViT-L/16</th><th>ViT-L/32</th></tr></thead><tbody><tr><td>10</td><td>0.86</td><td>0.91</td><td><strong>0.92</strong></td><td>0.87</td><td>0.84</td><td>0.83</td></tr><tr><td>20</td><td>0.83</td><td>0.87</td><td><strong>0.88</strong></td><td>0.82</td><td>0.83</td><td>0.80</td></tr><tr><td>30</td><td>0.82</td><td>0.87</td><td><strong>0.87</strong></td><td>0.81</td><td>0.81</td><td>0.77</td></tr><tr><td>40</td><td>0.81</td><td>0.87</td><td><strong>0.85</strong></td><td>0.79</td><td>0.79</td><td>0.75</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** (a) Time taken by various distance metrics for ViT-B/16, [b]: Time taken by various distance metrics for ViT-L/16, [c]: Time taken by various distance metrics by various metrics for ViT-L/32.</figcaption></figure>

# 5 Conclusion

This study investigates the performance of various deep learning based features and Vision Transformer (ViT) features for the task of content based satellite image retrieval problem. We have presented a detailed investigation of deep model & Vision Transformer based features for satellite image retrieval application. We have carried out exhaustive experiments by employing deep CNN and ViT features with various similarity metrics. We also evaluated deep CNN and ViT features with different type distance metrics. It was evident from our study that deep CNN with ball tree achieved the best performance on most of benchmark dataset. It can clearly evident from experiments ViT with ball tree data structure also achieved good performance with minor reduction in accuracy. Another insight we can draw conclusion from the above study that the deep models have outperformed the state-of-the-art Vision transformer (ViT) variants. In future, we focus on employing feature selection methods to select only relevant feature, which in turn will be more accurate and quicker in retrieval of images.
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