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In recent years, convolutional neural network (CNN)-based methods have been widely used for remote sensing (RS) scene classification tasks and have achieved excellent results. However, CNNs are not good at exploring contextual information, which is essential for fully understanding RS scenes. A new model named transformer attracts researchers’ attention to address this problem, which is skilled in mining the latent contextual information in RS scenes. Nevertheless, since the contents of RS scenes are diverse in type and various in scale, the performance of the original transformer in RS scene classification cannot reach what we expect. In addition, due to the specific self-attention mechanism, the time costs of the transformer are high, which hinders its practicability in the RS community. To overcome the above limitations, we propose a new model named efficient multiscale transformer and cross-level attention learning (EMTCAL) for RS scene classification in this article. EMTCAL combines the advantages of CNN and transformer to mine information within RS scenes fully. First, it uses a multilayer feature extraction module (MFEM) to acquire global visual features and multilevel convolutional features from RS scenes. Second, a contextual information extraction module (CIEM) is proposed to capture rich contextual information from multilevel features. In CIEM, taking the characteristics of RS scenes and the computational complexity into account, we propose an efficient multiscale transformer (EMST). EMST can mine the abundant knowledge with various scales hidden in RS scenes and model their inherent relations at small time costs. Third, a cross-level attention module (CLAM) is developed to aggregate and explore correlations of multilevel features. Finally, a class score fusion module (CSFM) is designed to integrate the contributions of global and aggregated multilevel features for discriminative scene representations. Extensive experiments are conducted on three public RS scene datasets. The positive results demonstrate that our EMTCAL can achieve superior classification performance and outperform many state-of-the-art methods. Our source codes are available in https://github.com/TangXu-Group/Remote-Sensing-Images-Classification/tree/main/EMTCAL. [ABSTRACT FROM AUTHOR]

With the explosive growth of the volume and resolution of high-resolution remote-sensing (HRRS) images, the management of them becomes a challenging task. The traditional content-based remote-sensing image retrieval (CBRSIR) technologies cannot meet what we expect due to the large volume of image archives and complex contents within HRRS images. As a successful approximate nearest neighborhood (ANN) search technique, Hash learning has received wide attention, especially when deep convolutional neural networks (DCNNs) appear. Due to DCNNs’ strong capacity of feature learning, many DCNN-based hashing methods have been proposed and achieved good performance for large-scale CBRSIR tasks. Nevertheless, their limitation is that a large of labeled training samples should be collected for training the deep models. To overcome this limitation, this article, therefore, develops a new supervised hash learning method for the large-scale HRRS CBRSIR task based on meta-learning, which could achieve well-retrieval performance with a few labeled training samples. First, taking the characteristics of HRRS into account, we develop a self-adaptive convolution (SAP-Conv) block and design a hashing net based on the block. SAP-Conv can learn robust features from HRRS images by exploring their multiscale information. Second, to enhance the generalization of the hashing net under a few labeled training samples, the hash learning is formulated in a meta-way, and we name it meta-hashing. Meta-hashing can effectively preserve the similarities between support and query set, and the similarities between samples within support set by the developed loss function. To further improve the performance of meta-hashing, we expand it to a dynamic version named dynamic-meta-hashing, in which the numbers of support and query are changeable in the training phase. Experimental results counted on the three widely used HRRS datasets demonstrate our dynamic-meta-hashing and meta-hashing can achieve promising performance in large-scale HRRS CBRSIR tasks based on a few training samples. Our source codes are available at https://github.com/TangXu-Group/Meta-hashing. [ABSTRACT FROM AUTHOR]

Ship detection plays a significant role in the high-resolution remote sensing (HRRS) community, but it is a challenging task due to the complex contents within HRRS images and the diverse orientation of ships. Recently, with the development of deep learning, the performance of the HRRS ship detection model has been improved greatly. Most of them employ deep networks and complicate anchor mechanism to get well ship detection results. Nevertheless, this kind of combination limits the detection efficiency. To address this problem, a new approach named accurate and real-time rotational ship detector (AR2Det) is proposed in this article to detect ships without the anchor mechanism. Based on the extracted features by the feature extraction module (FEM) and the central information of ships, AR2Det adopts two simple modules, ship detector (SDet) and center detector (CDet), to generate and improve the detection results, respectively. AR2Det is efficient due to the simple postprocessing and the lightweight network. Also, AR2Det performs satisfactorily due to the effective generation and enhancement strategy of bounding boxes. The extensive experiments are conducted on a public HRRS image ship detection dataset HRSC2016. The promising results show that our method outperforms the state-of-the-art approaches in terms of both accuracy and speed. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the hottest issues in remote sensing, where studies on pixel-level information and relationship are of great significance. In this article, graph convolutional network (GCN) is employed to accomplish this pixel-level task benefiting from its excellent capability in structure exploration and information propagation between different pixels. To reduce the communication burden between various PolSAR pixels and high computational cost for the whole PolSAR image, an adaptive GCN (AdapGCN) consisting of pixel-centered subgraphs is proposed in this article. In the AdapGCN, a data-adaptive kernel and a spatial-adaptive kernel are introduced to, respectively, model data structure and spatial structure for PolSAR image. Moreover, a multiscale learning structure is integrated to further explore complicated relations between pixels. Extensive comparative evaluations validate the superiority of our new AdapGCN model for PolSAR image classification over a wide range of state-of-the-art methods on three challenging benchmarks. [ABSTRACT FROM AUTHOR]

The content-based remote sensing image retrieval (CBRSIR) has attracted increasing attention with the number of remote sensing (RS) images growing explosively. Benefiting from the strong capacity of the deep convolutional neural network (DCNN), the performance of CBRSIR has been improved in recent years. Although great successes have been obtained, learning the RS images’ representative features and enhancing the retrieval efficiency for the large-scale CBRSIR tasks are still two challenging problems. In this article, we propose a new CBRSIR method named feature and hash (FAH) learning, which consists of a deep feature learning model (DFLM) and an adversarial hash learning model (AHLM). The DFLM aims at learning the RS images’ dense features to guarantee the retrieval precision. In the DFLM, the DCNN and the proposed feature aggregation are integrated to capture the multiscale features. Then, the discrimination of the obtained features can be highlighted by the attention map in the developed attention branch. The AHLM maps the dense features onto the compact hash codes so that the retrieval efficiency can be improved. The AHLM contains a hash learning submodel and an adversarial regularization submodel. In particular, the hash learning submodel learns the real-valued hash codes that are similarity preserved by semantic supervisions. The adversarial regularization submodel regularizes the real-valued hash codes to learn the discrete uniform distribution with possible values 0 and 1. In this way, the hash codes are coding-balanced and the quantization errors are reduced. Encouraging experimental results counted on three public benchmark data sets demonstrate that our FAH can achieve competitive performance in the CBRSIR task compared with many existing hash learning methods. [ABSTRACT FROM AUTHOR]

In recent years, with the development of deep learning (DL), the hyperspectral image (HSI) classification methods based on DL have shown superior performance. Although these DL-based methods have great successes, there is still room to improve their ability to explore spatial–spectral information. In this article, we propose a 3-D octave convolution with the spatial–spectral attention network (3DOC-SSAN) to capture discriminative spatial–spectral features for the classification of HSIs. Especially, we first extend the octave convolution model using 3-D convolution, namely, a 3-D octave convolution model (3D-OCM), in which four 3-D octave convolution blocks are combined to capture spatial–spectral features from HSIs. Not only the spatial information can be mined deeply from the high- and low-frequency aspects but also the spectral information can be taken into account by our 3D-OCM. Second, we introduce two attention models from spatial and spectral dimensions to highlight the important spatial areas and specific spectral bands that consist of significant information for the classification tasks. Finally, in order to integrate spatial and spectral information, we design an information complement model to transmit important information between spatial and spectral attention features. Through the information complement model, the beneficial parts of spatial and spectral attention features for the classification tasks can be fully utilized. Comparing with several existing popular classifiers, our proposed method can achieve competitive performance on four benchmark data sets. [ABSTRACT FROM AUTHOR]

A new method based on the Looking-Around-and-Into (LAaI) mode is proposed for the task of change detection in large-scope Polarimetric Synthetic Aperture Radar (PolSAR) image. Specifically, the LAaI mode consists of two processes named Look-Around and Look-Into, which are accomplished by attention proposal network (APN) and recurrent convolutional neural network (CNN) (Recurrent CNN), respectively. The former provides certain subregions efficiently, and the latter detects changes in subregions accurately. In Look-Around, difference image (DI) of whole PolSAR images is calculated first to get global information; then, APN is established to locate the position of interested subregions intentionally by paying special attention to; next interested subregions that contain changed area in high probability are picked out as candidate-regions. Moreover, candidate-regions are sorted in importance descending order so that highly interested regions have priority to be detected. In Look-Into, candidate-regions of different scales are selected at first; then, Recurrent CNN is constructed and employed to deal with multiscale PolSAR subimages so that clearer and finer change detection results are generated. The process is repeated until all candidate-regions are detected. As a whole, the proposed algorithm based on the LAaI mode looks around whole images first to find out the possible position of changes (candidate-regions generation in Look-Around) and then reveal the exact shape of changes in different scales (multiscale change detection in Look-Into). The effect of APN and Recurrent CNN is verified in experiments, and it shows that the proposed method performs well in the task of change detection in the large-scope PolSAR image. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) unmixing is an important issue of research due to its effect on the subsequent processing of HSIs. Recently, the sparse regression method with spatial information has been successfully applied in hyperspectral unmixing (HU). However, most sparse regression methods ignore the difference in spatial structure handling with only one sparse constraint. In fact, the pixels in detail regions are more likely to be severely mixed with more endmembers participated, and the sparsity degree of its corresponding abundances is relatively low. Considering the sparsity difference of abundances, a sketch-based region adaptive sparse unmixing applied to HSI is proposed in this article. Inspired by the vision computing theory, we use the region generation algorithm based on a sketch map to differentiate the homogeneous regions and detail regions. Then, the abundances of these two kind regions in HSIs are separately constrained by sparse regularizers of ${L}_{1/2}$ and ${L}_{1}$ with a proposed manifold constraint. Our method not only makes full use of the spatial information in HSIs but also exploits the latent structure of data. The encouraging experimental results on three data sets validate the effectiveness of our method for HU. [ABSTRACT FROM AUTHOR]