Recently, the hypergraph convolutional network (HGCN) has attracted increasing attention in hyperspectral image (HSI) classification. Compared to graph convolutional networks, HGCN has a stronger ability to mine nonlinear high-order correlations. However, the problems of intraclass variability and interclass similarity exist due to the effects of light, environment, and sensor bias, resulting in insufficient reliability of hypergraphs constructed by directly utilizing the original spectral features. Motivated by the observation that the land cover in HSI contains the spatial distribution semantic information of community structures, which can be used to extract deeper contextual semantic features, we propose a novel community structure guided network (CSGNet) for HSI classification. Specifically, CSGNet adopts a dual-branch architecture: the HGCN branch focuses on superpixel-level high-order feature extraction, while the convolutional neural network (CNN) branch enhances pixel-level local features. In HGCN branch, a novel reliable hypergraph construction approach is introduced, which strikes a balance between depth-first search (DFS) and breadth-first search (BFS), effectively representing different community structure features and improving the ability of edge detection. Meanwhile, kernel function mapping is used to achieve more accurate node connections and enhances classification within classes. Finally, to achieve balanced training of the HGCN and CNN branches, we add their cross-entropy loss as an auxiliary component in the backpropagation process. Experimental results demonstrate that CSGNet outperforms the state-of-the-art methods. The code will be released athttps://github.com/KustTeamWQW/CSGNet.

With the development of remote sensing technology for earth observation, the collaborative utilization of Hyperspectral images (HSI) and multispectral images (MSI) has received increasing attention in terrestrial observation. HSI and MSI represent two typical types of optical remote sensing data and can provide rich complementary information. However, the paradoxical problem of high spatial resolution and high spectral resolution leads to difficulties in extracting complementary information. In this paper, we propose an Adaptive Feature Exchange network with Complementary Advantages (AFECAnet). Specifically, we enhance the discriminative feature extraction of HSI-MSI by introducing a spectral-spatial feature enhancement module based on a dual attention mechanism. Subsequently, in order to reduce redundant information, we design an adaptive feature interaction strategy based on batch normalization privatization factors. This strategy helps to accurately replace redundant information and reduces the computational burden on the network. Experimental results demonstrate that the proposed AFECAnet has a significant improvement in HSI-MSI collaborative classification.

Recently, some hybrid networks, combining graph convolutional network (GCN) and convolutional neural network (CNN) into a unified framework, have drawn increasing attention in hyperspectral image (HSI) classification. Compared with the single CNN or GCN architecture, hybrid networks can simultaneously perform feature learning on pixel-level and superpixel-level regions and generate complementary spectral-spatial features. However, existing methods primarily employ simple fusion strategies such as linear combination or concatenation, resulting in the extracted complementary features not being fully exploited and utilized. In this work, we propose a differential feature-enhanced fusion network (DFEFN) for HSI classification. Specifically, DFEFN consists of two different convolutional network architectures, (i.e. GCN and CNN), and a differential feature enhancement fusion (DFEF) module. The features extracted by CNN and GCN can be enhanced and fused through the DFEF module. Experiments results on two benchmark HSI datasets demonstrate that DFEFN achieves better classification performance compared with state-of-the-art methods.

Recently, the hypergraph neural network (HGNN) has drawn increasing attention in modeling complex high-order correlations. Compared to simple graph neural networks, HGNNs exhibit more powerful representational ability. There are two limitations in the application of hypergraph theory to hyperspectral image (HSI) classification. One is the inadequate explicit representation of semantic information contained in HSI. Another is the loss of pixel-level spectral-spatial information. Thus, an enhanced hypergraph neural network (EHGNN) is proposed to promote the application of hypergraph theory to HSI classification. Specifically, two important enhancements are introduced: 1) the concept of key hypergraph, providing more rich semantic information and improving the interpretability for complex distribution structures, and 2) the integration of convolutional neural network (CNN) and HGNN architectures into an end-to-end framework, the loss of spectral-spatial information at the pixel-level is effectively reduced. Through these two enhancements, EHGNN exhibits a 4% improvement in overall accuracy (OA) on the Pavia University dataset and a 2% improvement in OA on the Xuzhou dataset compared to HGNN. Furthermore, the test results on two HSI datasets demonstrate that our EHGNN achieves competitive performance compared to other state-of-the-art methods.

Remote sensing cross-scene classification has always been an important research field, especially in the field of 3-D classification, which is of great significance. Considering the diversity of collection conditions, seasons, and regional styles, deep learning networks well-trained on one source domain dataset tend to suffer from severe performance degradation when applied to other target domain datasets. To tackle the issue, in this article, we propose a new cross-scene classification method, which combines pre-alignment and Shannon entropy constraint to accomplish unsupervised domain adaptive classification (PS-UDA). On the one hand, the pre-alignment employs$L_{2}$-paradigm constraint and Laplace matrix to pre-align the features. With the$L_{2}$-paradigm constraint, the originally distant features of the source and target domain are constrained to the same sphere surface, and it is easier to make the distribution alignment on the sphere surface. Further, the Laplace matrix is used to map the source and target domain. In this way, similar features of the source and target domain are further aligned, and dissimilar features become discrete from each other. On the other hand, this article employs the Shannon entropy constraint to motivate the network to obtain more high-confidence target domain pseudo-labels. In addition, to fully utilize the unlabeled target domain information, the target domain features are augmented using the adjacency matrix. Experimental results of two cross-scene multispectral point cloud classifications demonstrate that the proposed PS-UDA can effectively mitigate the spectral shift issue in cross-scene multispectral point clouds, achieving state-of-the-art performance.