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Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey: first, autoencoder-based remote sensing image scene classification methods; second, convolutional neural network-based remote sensing image scene classification methods; and third, generative adversarial network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly used benchmark datasets. Finally, we discuss the promising opportunities for further research.

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

In this paper, we propose a remote-sensing scene-classification method based on vision transformers. These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in standard convolutional neural networks (CNNs). Instead, they use multihead attention mechanisms as the main building block to derive long-range contextual relation between pixels in images. In a first step, the images under analysis are divided into patches, then converted to sequence by flattening and embedding. To keep information about the position, embedding position is added to these patches. Then, the resulting sequence is fed to several multihead attention layers for generating the final representation. At the classification stage, the first token sequence is fed to a softmax classification layer. To boost the classification performance, we explore several data augmentation strategies to generate additional data for training. Moreover, we show experimentally that we can compress the network by pruning half of the layers while keeping competing classification accuracies. Experimental results conducted on different remote-sensing image datasets demonstrate the promising capability of the model compared to state-of-the-art methods. Specifically, Vision Transformer obtains an average classification accuracy of 98.49%, 95.86%, 95.56% and 93.83% on Merced, AID, Optimal31 and NWPU datasets, respectively. While the compressed version obtained by removing half of the multihead attention layers yields 97.90%, 94.27%, 95.30% and 93.05%, respectively.

Vision Transformers for Remote Sensing Image Classification

Image retrieval from remote sensing big data: A survey

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many efforts have been made to improve the accuracy of RS scene classification. Scene classification is a challenging problem, especially for large datasets with tens of thousands of images with a large number of classes and taken under different circumstances. One problem that is observed in scene classification is the fact that for a given scene, only one part of it indicates which class it belongs to, whereas the other parts are either irrelevant or they actually tend to belong to another class. To address this issue, this paper proposes a deep attention Convolutional Neural Network (CNN) for scene classification in remote sensing. CNN models use successive convolutional layers to learn feature maps from larger and larger regions (or receptive fields) of the scene. The attention mechanism computes a new feature map as a weighted average of these original feature maps. In particular, we propose a solution, named EfficientNet-B3-Attn-2, based on the pre-trained EfficientNet-B3 CNN enhanced with an attention mechanism. A dedicated branch is added to layer 262 of the network, to compute the required weights. These weights are learned automatically by training the whole CNN model end-to-end using the backpropagation algorithm. In this way, the network learns to emphasize important regions of the scene and suppress the regions that are irrelevant to the classification. We tested the proposed EfficientNet-B3-Attn-2 on six popular remote sensing datasets, namely UC Merced, KSA, OPTIMAL-31, RSSCN7, WHU-RS19, and AID datasets, showing its strong capabilities in classifying RS scenes.

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Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

In convolutional neural networks (CNNs), higher layer information is more abstract and more task specific, so people usually concern themselves with fully connected (FC) layer features, believing that lower layer features are less discriminative. However, a few researchers showed that the lower layers also provide very rich and powerful information for image representation. In view of these study findings, in this letter, we attempt to adaptively and explicitly combine the activations from intermediate and FC layers to generate a new CNN with directed acyclic graph topology, which is called the converted CNN. After that, two converted CNNs are integrated together to further improve the classification performance. We validate our proposed two-stage deep feature fusion model over two publicly available remote sensing data sets, and achieve a state-of-the-art performance in scene classification tasks.

Scene Classification Based on Two-Stage Deep Feature Fusion

In multiclass classification, convolutional neural network (CNN) is generally coupled with the cross-entropy (CE) loss, which only penalizes the predicted probability corresponding to a ground truth class and ignores the interclass relationship. We argue that CNN can be improved by using a better loss function. On the other hand, the Wasserstein distance (WD) is a well-known metric used to measure the distance between two distributions. Directly solving the WD problem requires a prohibitively large amount of computation time, whereas the cheaper iterative algorithms have a variety of shortcomings such as computational instability and difficulty in selecting parameters. In this paper, we address these issues by giving an analytical solution to the WD problem—for the first time, we find that for two distributions in hierarchically organized data space, WD has a closed-form solution, which we call “hierarchical WD (HWD).” We use this theory to construct novel loss functions that overcome the shortcomings of CE loss. To this end, multi-CNN information fusion that provides the basis for building category hierarchies is carried out first. Then, the semantic relationship among classes is modeled as a binary tree. Then, CNN coupled with an HWD-based loss, i.e., hierarchical Wasserstein CNN (HW-CNN), is trained to learn deep features. In this way, prior knowledge about the interclass relationship is embedded into HW-CNN, and information from several CNNs provides guidance in the process of training individual HW-CNNs. We conducted extensive experiments over two publicly available remote sensing data sets and achieved a state-of-the-art performance in scene classification tasks.

Scene Classification Using Hierarchical Wasserstein CNN

In the field of content-based remote sensing (RS) image retrieval, convolutional neural networks (CNNs) have been demonstrating overwhelming superiority among other methods in terms of performance. CNNs are basically trained in a supervised way, requiring a larger number of labeled samples. However, scarcity of labeled images is very prevalent in the RS community. Moreover, CNN-based approaches have other disadvantages such as cumbersome networks and high-dimensional features. To address these issues, we apply unsupervised transfer learning to CNN training—we transform similarity learning into deep ordinal classification with the help of several CNN experts pretrained over large-scale-labeled everyday image sets, which jointly determine image similarities and provide pseudolabels for classification. Our proposed method ends up with a brand-new lightweight model called similarity-based Siamese CNN (SBS-CNN), which can be trained from scratch with completely unlabeled RS images, and whose resulting features are compact. Furthermore, the existing CNNs are generally coupled with the cross-entropy loss, entirely ignoring the interclass semantic relationship. To overcome this shortcoming, a novel loss function called weighted Wasserstein ordinal loss is constructed to take into account the ordinal relationship among categories, thus more effectively navigating parameter updates during training. Extensive experiments have been carried out over publicly available RS data sets, and it turns out that our SBS-CNN outperforms existing CNN-based approaches.

Similarity-Based Unsupervised Deep Transfer Learning for Remote Sensing Image Retrieval

In recent years, convolutional neural networks (CNNs) have become the predominant method for content-based aerial image retrieval (CBAIR) and aerial scene classification (ASC) due to their overwhelming performance advantages. However, existing CNN-based models have the following shortcomings: first, they do not deal with large intraclass variations, thereby overlooking the possibility of fine-grained retrieval and classification; second, all similarity learning methods for CBAIR consider similarity between two images as a constant, neglecting the fact that image similarity is uncertain in nature; third, similarity learning is separated from ASC, ignoring the advantages of joint optimization. To address these issues, we propose a novel metric learning method called center-metric learning, and couple it with a new kind of loss called positive-negative center loss, which, with the help of several “experts,” enables CNNs to cope successfully with within-class variations. Besides, we propose similarity distribution learning, making the first attempt to embed uncertainty regarding similarity into the training process. The resulting fine-grained similarity predictions can further strengthen CNNs’ fine discrimination ability. Furthermore, three tasks, that is, center-metric learning, similarity distribution learning, and ASC, are incorporated into one CNN, benefitting from one another and leading to a better generalization capability. Just like an eagle, our model is able to discriminate subtle differences among aerial images, hence the name “eagle-eyed multitask CNN.” We carry out extensive experiments over four publicly available aerial image sets and achieve a performance better than all existing methods.

Eagle-Eyed Multitask CNNs for Aerial Image Retrieval and Scene Classification

In high-resolution remote sensing image retrieval (HRRSIR), convolutional neural networks (CNNs) have an absolute performance advantage over the traditional hand-crafted features. However, some CNN-based HRRSIR models are classification-oriented, they pay no attention to similarity, which is critical to image retrieval; whereas others concentrate on learning similarity, failing to take full advantage of information about class labels. To address these issues, we propose a novel model called classification-similarity network (CSN) , which aims for image classification and similarity prediction at the same time. In order to further improve performance, we build and train two CSNs, and two kinds of information from them, i.e., deep features and similarity scores, are consolidated to measure the final similarity between two images. Besides, the optimal fusion theorem in biometric authentication, which gives a theoretical scheme to make sure that fusion will definitely lead to a better performance, is used to conduct score fusion. Extensive experiments are carried out over publicly available datasets, demonstrating that CSNs are distinctly superior to usual CNNs and our proposed “two CSNs + feature fusion + score fusion” method outperforms the state-of-the-art models.

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Yingbin Liu

Papers

Scene Classification Based on Two-Stage Deep Feature Fusion

Scene Classification Using Hierarchical Wasserstein CNN

Similarity-Based Unsupervised Deep Transfer Learning for Remote Sensing Image Retrieval

Eagle-Eyed Multitask CNNs for Aerial Image Retrieval and Scene Classification

High-Resolution Remote Sensing Image Retrieval Based on Classification-Similarity Networks and Double Fusion