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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]

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral data analysis. Convolutional neural networks (CNN) have been introduced to HSI classification and achieved good performance. In this article, an effective and efficient CNN-based spectral partitioning residual network (SPRN) is proposed for HSI classification. The SPRN splits the input spectral bands into several nonoverlapping continuous subbands and uses cascaded parallel improved residual blocks to extract spectral–spatial features from these subbands, respectively. Finally, the features are fused and fed into a classifier. By equivalently using grouped convolutions, the spectral partition and feature extraction are embedded into an end-to-end network. Experimental results show that the proposed SPRN achieves state-of-the-art performance, meanwhile, with relatively fewer parameters and computational costs. Usually, the CNN takes a patch that contains continuous spatial information as the input and results in a class label of the center pixel. The large size of the input patch includes more spatial information, whereas also introduces interfering pixels that may lead to a degradation of classification accuracies. For that reason, we propose a novel spatial attention module named homogeneous pixel detection module (HPDM). The module alleviates the degradation of performance as the input patch size increases by capturing the homogeneous pixels in the input patch. The module can be integrated into any CNN-based HSI classification framework. [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]

Recently, multicategory object detection in high-resolution remote sensing images is still a challenge. First, objects with significant scale differences exist in one scene simultaneously, so it is generally difficult for the detectors to balance the detection performance of large and small objects. Second, because of the complex background and the objects’ densely distributed characteristics in the remote sensing images, the extracted features usually have noise and blurred boundaries, which interfere with the detection performance of the object detectors. With this observation, we propose an end-to-end scale-aware network called feature split–merge–enhancement network (SME-Net) for remote sensing object detection, composed of the feature split-and-merge (FSM) module, the offset-error rectification (OER) module, and the object saliency enhancement (OSE) strategy. FSM eliminates salient information of large objects to highlight the features of small objects in the shallow feature maps. It also transmits the effective detailed features of large objects to the deep feature maps, alleviating feature confusion between multiscale objects. OER corrects the inconsistency of the features spatial layout among the multilayer feature maps by the proposed offset loss, so as to achieve supervised elimination and transmission in FSM. OSE enhances the features of interests and suppresses the background information by the proposed membership function, thus preventing false detection and missed detection caused by noise and blurred boundaries. The effectiveness of the proposed algorithm has been verified on multiple datasets. Our code is available at: https://github.com/Momuli/SMENet.git [ABSTRACT FROM AUTHOR]

Graph convolution networks (GCNs) have been applied in a variety of fields due to their powerful ability in processing graph-like data. However, the massive number of hyperspectral pixels makes it challenging to define general graph structures on hyperspectral images (HSIs). On the other hand, convolutional neural networks (CNNs) take in regular image regions with fixed square size, and have demonstrated impressive accuracy while being efficient in computation. Inspired by the classification framework of CNNs, we develop a GCN-based model that generates effective local spectral–spatial features for HSI classification. Specifically, graph convolutions are performed separately on every local region, which significantly limits the graph’s size. While graph convolution extracts features of every pixel, it does not reduce the number of them. To fuse suitable representations for the classification task, we develop a graph pooling operation to preserve classification-specific features and reduce redundant pixels. Based on local regions of HSIs, pooling in the graph domain is equivalent to spatial pooling in the spatial domain. The proposed method is thus named the spatial pooling graph convolutional network (SPGCN). Experimental results on several typical datasets demonstrated that the proposed SPGCN provides competitive results compared with other state-of-the-art CNN-based methods. [ABSTRACT FROM AUTHOR]

Anomaly detection of a hyperspectral image without any prior information has attracted much more attention in remote sensing image understanding and interpretation, which aims at determining whether a sample belongs to background or anomaly. Low-rank dictionary learning plays an important role in exploiting the low-rank prior of background for hyperspectral image (HSI) anomaly detection. In this article, the low-rank dictionary learning is introduced to learn a dictionary which can reconstruct the background positively, while anomaly cannot. Considering the high correlation of data especially between the adjacent bands, we resort to spectral-difference low-rank dictionary representation learning for global background modeling which can fully exploit the low-rank prior of background. Then, the residual matrix is used to distinguish anomaly. Different from the existing anomaly detection methods based on dictionary which is constructed or learned in a separated step, our proposed model can simultaneously learn the dictionary and separate anomaly by iterative learning. The experimental results on five real data sets demonstrate the superior performance of the proposed method for hyperspectral anomaly detection compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]