Hyperspectral image (HSI) classification has always been one of the hot issues in the study of geographic remote sensing information, and graph neural networks have attracted much attention in recent years. Several graph neural network-based approaches have been introduced into HSI study to explore the spatial information of HSI within a constructed graph. However, the existing methods of building HSI-based graphs are always unsuitable and inaccurate due to the complicated spatial variability of spectral signatures. Meanwhile, these graph-based HSI classification methods usually suffer from the over-smoothing problem. Motivated by these, this article presents a novel deep network with adaptive graph structure integration (DNAGSI), which could learn a graph structure of HSI dynamically and promote its discriminative ability with devising a much deeper network architecture. Specifically, dynamic graphs are first built across different layers and adaptively integrated with the initial graph structure to boost the robust graph representation of HSI. Second, the initial residual and identity mapping are employed to significantly increase the depth of the network and obtain more abstract deep features. Finally, a joint loss with center loss is devised to learn the similarity relationship between hyperspectral pixels explicitly, thereby gathering the intraclass graph features. Benefiting from the integration of center loss, initial residual, and identity mapping, the proposed method can alleviate the over-smoothing problem effectively to some extent. Experiments on benchmark HSI datasets demonstrate the superiority of DNAGSI over state-of-the-art methods.

Hyperspectral image (HSI) change detection (CD) plays a crucial role in remote sensing observation. It leverages the abundant spectral and spatial information in bi-temporal HSIs to identify subtle Earth surface changes. Most current deep learning-based HSI CD methods primarily utilize convolutional neural networks or transformers to extract features from bitemporal images. However, these methods lack an effective attention mechanism to enhance differential features. Additionally, they do not fully leverage the aggregation relationship between the features of bi-temporal images to extract interaction features. To address these challenges, we propose a novel adaptive information weighting and synchronized enhancement network (AIWSEN) for HSI CD. This network employs the information entropy to capture change features specific to the CD task and enhances bi-temporal interaction features. Specifically, an adaptive information weighting attention module (AIWAM) leverages the maximum discrete entropy theorem to capture the difference information. A dual-time synchronic change enhancing module (DSCEM) is designed to extract features by interactively aggregating features from bi-temporal HSIs to enhance difference features. A bi-temporal image feature selection and fusion module (BFSFM) is constructed to filter out important features using forget and update gates. Experimental results on three HSI CD datasets demonstrate that the proposed AIWSEN method outperforms several state-of-the-art methods. The source code of the proposed AIWSEN will be released at https://github.com/creativeXin/AIWSEN.

Hyperspectral image (HSI) change detection (CD) has gained significant attention in the field of remote sensing. Current CD methods typically extract features based on spatial or spectral correlations between bitemporal HSIs, which often overlook the difference information, leading to a decrease in CD accuracy. Furthermore, these algorithms do not fully consider the alignment of features between images across different channel and spatial dimensions. To tackle these issues, we propose a novel approach called the Differential Information-Enhanced Feature Exchange Network (DIEFEN) for HSI CD, which leverages the difference information between images and enhances the alignment of bitemporal image features to improve change detection accuracy. Specifically, an enhanced differential multi-head attention (EDMA) module is proposed to utilize difference information to guide the feature aggregation of bitemporal images, effectively highlighting changing pixels and suppressing unchanging pixels. A feature focus and long-range attention (FFLA) module is designed to extract local and global features, and a channel-spatial interaction (CSI) module is constructed to align features and mitigate the impact of noise. Experimental results on three HSI CD datasets demonstrate that the proposed DIEFEN method outperforms several state-of-the-art methods. The source code of the proposed DIEFEN is released at https:// github.com/creativeXin/DIEFEN.

Graph neural networks (GNNs) have garnered significant attention in hyperspectral image (HSI) change detection (CD). However, existing GNN-based methods extract features by aggregating neighborhood information, which is essentially a low-pass Laplacian smoothing operation and tends to diminish change information between bitemporal HSIs. In addition, these methods rely on fixed hand-crafted graphs, and thus cannot capture complex structures of HSIs well. To address these deficiencies, this paper develops a Sharpening-Aware Graph Network (SAGN) for achieving high-quality HSI CD. Firstly, to counteract the weakening of differences caused by Laplacian smoothing, this paper proposes a novel Laplacian sharpening-based graph convolution (LSGC) module to accentuate change information between bitemporal HSIs. Secondly, instead of using “similarity graphs”, this paper constructs untied “difference graphs” for bitemporal HSIs to model dissimilarities between changed pixels and their neighbors. The SAGN can dynamically update graph structures across all layers, aiming to further maximize the divergence. Finally, a joint loss function, incorporating modified cross-entropy loss and contrastive loss, is devised to enhance inter-class discrimination of learned features and alleviate the issues stemming from imbalanced labeled samples. Experiments on various HSI CD datasets demonstrate the effectiveness and superiority of the proposed SAGN.

Graph convolutional networks (GCNs) have attracted significant attention in hyperspectral image (HSI) change detection (CD) due to their capability to perform shape-adaptive convolutions and capture complex patterns within HSIs. Existing GCN-based methods typically preprocess bitemporal HSIs into graphs using a specific superpixel segmentation. However, this preprocessing step limits the modeling of spatial topologies to a fixed scale. Besides, these methods do not consider distribution shifts between bitemporal HSIs. To overcome these limitations, this paper proposes a fine-coarse-fine progressive graph framework with distribution alignment (FCFDA) to learn progressive features across multilevel graphs for HSI-CD. Specifically, for each of bitemporal HSIs, we generate multiple hierarchical segmentations ranging from fine to coarse by gradually merging neighboring superpixels and subsequently transform these segmentations into multilevel graphs. Second, instead of simply concatenating features from different hierarchies, FCFDA integrates them progressively from fine to coarse and then back to fine, generating subtle features tailored to the pixelwise CD task. Finally, an effective distribution alignment method is designed to align the feature space of the bitemporal HSIs, thus mitigating the adverse effects of distribution shifts. Experiments conducted on real HSI-CD datasets demonstrate the effectiveness and superiority of the FCFDA.