Hyperspectral image (HSI) classification is crucial in the remote sensing (RS) community. In recent years, Transformers have been popular in this field due to their global information modeling capabilities. However, the quadratic complexity limits their performance under limited computational resources. Fortunately, a selective structured state space model named Mamba emerges. Like Transformer, it is good at modeling the long-distance relationships hidden in the pending data. Unlike Transformer, its complexity remains at a linear level. Therefore, a growing number of studies have been proposed to explore the usefulness of Mamba in HSI classification. Nevertheless, most of them only apply Mamba to HSIs directly but do not consider the inherent characteristics of HSIs properly. To exploit the potential of Mamba in HSI classification deeply, this paper presents a new spatial-spectral complementary Mamba with a spatial spiral scan named SpiralMamba. It mainly encloses three main components: a spatial Mamba encoder (SpaME), a spectral Mamba encoder (SpeME), and a spatial-spectral complementary fusion module (SSCFM). SpaME focuses on understanding the spatial context within HSIs. To this end, instead of the common scanning, a spatial spiral scan strategy is introduced to address the sequence transformation of non-causal HSIs. SpeME aims to comprehensively extract valuable spectral features from HSIs. To achieve this goal, besides developing a spectral bidirectional scan strategy, a multilayer convolution (MLC) is also incorporated to capture local variations within spectral tokens. SSCFM concentrates on building the complex connections between spatial and spectral features and fusing them. For this purpose, a relationship learning block (RLB) and a threshold enhancement mechanism (TEM) are developed. Positive experimental results counted on three public HSI datasets demonstrate the effectiveness of SpiralMamba. Our source codes are available at https://github.com/TangXu-Group/Hyperspectral-Images-Classification/tree/main/SpiralMamba.

Semi-supervised learning aims to exploit the potential of unlabeled data to enhance model performance, which makes it suitable for addressing the challenge of limited labeled data. As a popular technology, pseudo-label is widely applied in many semi-supervised remote sensing (RS) change detection methods. However, when facing limited labeled data, abundant low-quality pseudo-labels from a poorly-performing model hinder the effective enhancement of model performance. To address this issue, we propose a novel semi-supervised strategy, named ensemble cross pseudo supervision (ECPS). The utilization of ensemble learning to merge outputs from several change detection models enhances pseudo-label quality, leading to more accurate change information and a significant boost in model performance, even with limited labeled data. In this method, adopting crosswise supervision ensures that no additional inference costs caused by ensemble learning are consumed. This provides both high efficiency and effectiveness for identifying land-cover changes. On the other hand, a simple yet effective ensemble strategy is proposed, which allows to manually adjust the model’s tendency towards higher precision or recall for satisfying practical requirements. We conduct extensive experiments on four public RS change detection datasets, and the promising results demonstrate the superiority of the proposed method across various numbers of labeled samples. Our source codes are available at https://github.com/TangXu-Group/ECPS.

As an important topic in the remote sensing (RS) image processing community, change detection has attracted much attention from researchers, which aims to distinguish land-cover changes in a geographic position. This is a challenging task because the visual representations of land cover captured from RS images at different periods would vary widely and considerably, resulting in significant differences in feature representations. To alleviate this problem, many existing deep-based methods employ the parameter-shared strategy to map RS images into a common feature space for detecting the changes. Although they are feasible, the simple and single visual information learned by deep models is still not sophisticated enough for satisfactory results. To address this problem, we propose a forward dictionary learning (DL) model named forward DL detector (FDLdet) in this article. Besides the common visual features, our FDLdet takes into account the essential information, e.g., element composition and land-cover category, for change detection. FDLdet consists of a feature extractor, a coefficient generator, and a deep dictionary. Specifically, first, the feature extractor is used to extract shared deep features from RS images. Second, the coefficient generator transforms these deep features into word coefficients. Third, words within the deep dictionary are combined by word coefficients to generate the dictionary features with essential information. Finally, the dictionary features are used instead of deep features to detect land-cover changes. Extensive experiments are conducted on two public large-scale datasets, i.e., season-varying change detection (SVCD), Sun Yat-sen University change detection (SYSU-CD), and LEVIR change detection (LEVIR-CD). Experimental results demonstrate the effectiveness of the proposed FDLdet. Our source codes are available athttps://github.com/TangXu-Group/FDLdet.

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking performance on hyperspectral image (HSI) classification tasks, due to its hierarchical structure and strong nonlinear fitting capacity. Most of them, however, are supervised approaches that need a large number of labeled data to train them. Conventional convolution kernels are fixed shape of rectangular with fixed sizes, which are good at capturing short-range relations between pixels within HSIs but ignore the long-range context within HSIs, limiting their performance. To overcome the limitations mentioned above, we present a dynamic multiscale graph convolutional network (GCN) classifier (DMSGer). DMSGer first constructs a relatively small graph at region-level based on a superpixel segmentation algorithm and metric-learning. A dynamic pixel-level feature update strategy is then applied to the region-level adjacency matrix, which can help DMSGer learn the pixel representation dynamically. Finally, to deeply understand the complex contents within HSIs, our model is expanded into a multiscale version. On the one hand, by introducing graph learning theory, DMSGer accomplishes HSI classification tasks in a semi-supervised manner, relieving the pressure of collecting abundant labeled samples. Superpixels are generally in irregular shapes and sizes which can group only similar pixels in a neighborhood. On the other hand, based on the proposed dynamic-GCN, the pixel-level and region-level information can be captured simultaneously in one graph convolution layer such that the classification results can be improved. Also, due to the proper multiscale expansion, more helpful information can be captured from HSIs. Extensive experiments were conducted on four public HSIs, and the promising results illustrate that our DMSGer is robust in classifying HSIs. Our source codes are available at https://github.com/TangXu-Group/DMSGer.

Change detection is developed to automatically identify semantic changes between remote sensing (RS) images captured at different points of time in a specific geographic location. Due to the limited availability of annotated data and the high complexity of the change detection problem, the performance of change detection models can not meet our expectations. To address this issue, many methods are proposed to solve this issue. However, ignoring the characteristics of the change detection task limits their performance. Therefore, we introduce a novel exchange data enhancement method (EDEM) strategy to generate image pairs to help the neural network to capture the temporal consistency in the data. We evaluate the proposed approach on two publicly available datasets and compare it with several state-of-the-art methods. The experimental results demonstrate that our proposed approach can effectively improve the performance of change detection models, achieving state-of-the-art performance on both datasets.

Few-shot remote sensing scene classification (FSRSSC) aims to make the model quickly adapt to new scenes with a small amount of annotation data. The large intra-class variance and high inter-class similarity in remote sensing (RS) scenes make this task more challenging. To this end, we propose a multi-scale interaction prototypical network, which pays attention to capturing multi-scale information of images during model learning, and then generates a prototype representation of mixed query information through a feature interaction module, thereby enhancing the rapid learning ability of the model, so as to reducing of the within-class and between-class variance ratio in RS scenes. The positive experimental results on UC-Merced and NWPU datasets demonstrate the effectiveness of our model in FSRSSC.

Synthetic aperture radar (SAR) image change detection is a hot but challenging task due to SAR images’ complex contents and inherent speckle noises. The expected change detection methods should reduce the influence of speckle noises, obtain the discriminative feature representations, and generate accurate change maps simultaneously. To these ends, we propose a new SAR image change detection method named feature fusion of information transfer network (FFITN). First, we develop a hybrid convolution block to depress the speckle noise impacts and explore the valuable information from SAR images. Thus, the feature extraction module (FEM) is constructed to obtain the multi-level features. Then, an information transfer module (ITM) is proposed to capture the salient regions from various aspects. Also, the salient knowledge is transferred among features at different levels to enhance their discrimination. Next, a self-attention-based feature fusion module (SAFFM) is introduced to fuse various features. Finally, a change map generation module (CMGM) with the clustering algorithm and specific loss functions is designed to produce the pseudo labels and change maps. Experimental results on three public SAR data sets demonstrate the model’s effectiveness. Our source codes are available at https://github.com/TangXu-Group/FFITN.

The scene classification of remote sensing (RS) images plays an essential role in the RS community, aiming to assign the semantics to different RS scenes. With the increase of spatial resolution of RS images, high-resolution RS (HRRS) image scene classification becomes a challenging task because the contents within HRRS images are diverse in type, various in scale, and massive in volume. Recently, deep convolution neural networks (DCNNs) provide the promising results of the HRRS scene classification. Most of them regard HRRS scene classification tasks as single-label problems. In this way, the semantics represented by the manual annotation decide the final classification results directly. Although it is feasible, the various semantics hidden in HRRS images are ignored, thus resulting in inaccurate decision. To overcome this limitation, we propose a semantic-aware graph network (SAGN) for HRRS images. SAGN consists of a dense feature pyramid network (DFPN), an adaptive semantic analysis module (ASAM), a dynamic graph feature update module, and a scene decision module (SDM). Their function is to extract the multi-scale information, mine the various semantics, exploit the unstructured relations between diverse semantics, and make the decision for HRRS scenes, respectively. Instead of transforming single-label problems into multi-label issues, our SAGN elaborates the proper methods to make full use of diverse semantics hidden in HRRS images to accomplish scene classification tasks. The extensive experiments are conducted on three popular HRRS scene data sets. Experimental results show the effectiveness of the proposed SAGN. Our source codes are available at https://github.com/TangXu-Group/SAGN.

As a hot topic in the field of remote sensing (RS), change detection aims to identify the semantic change between bitemporal RS images. Due to the semantic complexity of RS images, how to accurately detect the semantic change has become a challenging problem. Recently, many deep-based methods are proposed to solve this issue. However, ignoring the representation difference of same semantics in different periods limits their performance, such as river is liquid in summer and solid in winter. Therefore, a new method is presented, named dictionary learning based change detector (DLCDet), which consists of feature pyramid network, deep dictionary learning and dual supervision modules. In DLCDet, the deep dictionary learning is proposed to reduce the representation difference so that DLCDet identifies the potential semantic change more accurately. Experiments are conducted on two public datasets change detection dataset (CDD) and building change detection dataset (BCDD), which demonstrates the effectiveness of the proposed method.

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 athttps://github.com/TangXu-Group/Meta-hashing.

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.

As an important research topic in the remote sensing (RS) community, RS image scene classification is a challenging task due to the complex contents of RS images. In general, RS image scene classification is a single-label problem. Nevertheless, it is known that the contents within RS are huge in volume and diverse in type. Only a single semantic label cannot describe an RS scene completely, especially when the resolution of RS images is increased recently. The various semantics hidden in the high-resolution RS (HRRS) images are also important to the scene classification task. Taking the issues mentioned above into account, we develop a new scene classifier named graph scene classifier (GSCer) for HRRS images with the help of the deep convolution neural network (DCNN) and dynamic graph convolution (DGCN). Not only the global semantic but also the diverse hidden local semantics within an HRRS image can be fully explored. The encouraging experimental results counted on two public HRRS data sets demonstrate that our GSCer is effective in HRRS scene classification tasks.