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.

Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The generative adversarial network (GAN) is a promising technique to mitigate the small sample size problem. GAN can generate samples by the competition between a generator and a discriminator. However, it is difficult to generate high-quality samples for HSIs with complex spatial–spectral distribution, which may further degrade the performance of the discriminator. To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed. In CA-GAN, the generator and the discriminator not only compete but also collaborate. The shallow to deep features of real multiclass samples in the discriminator assist the sample generation in the generator. In the generator, a joint spatial–spectral hard attention module is devised by defining a dynamic activation function based on a multi-branch convolutional network. It impels the distribution of generated samples to approximate the distribution of real HSIs both in spectral and spatial dimensions, and it discards misleading and confounding information. In the discriminator, a convolutional LSTM layer is merged to extract spatial contextual features and capture long-term spectral dependencies simultaneously. Finally, the classification performance of the discriminator is improved by enforcing competitive and collaborative learning between the discriminator and generator. Experiments on HSI datasets show that CA-GAN obtains satisfactory classification results compared with advanced methods, especially when the number of training samples is limited.

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that typically operates without training labels. Recent advancements in deep graph clustering methods have shown promise for HSI due to their ability to effectively encode spatial structural information. However, limitations such as inadequate utilization of structural information, poor feature representation, and weak graph update capabilities hinder their performance. In this article, we propose an adaptive homophily structure graph clustering (AHSGC) method for HSI. Our approach begins with the generation of homogeneous regions to process HSI and construct the initial graph. Next, we design an adaptive filter graph encoder that captures both high and low-frequency features for subsequent processing. We then develop a graph embedding clustering self-training decoder using KL Divergence to generate pseudo-labels for network training. To enhance graph learning, we introduce homophily-enhanced structure learning, which updates the graph based on the clustering task. This involves estimating node connections through orient correlation estimation and dynamically adjusting graph edges via graph edge sparsification. Finally, we implement joint network optimization to facilitate self-training and graph updates, with K-means used to express latent features. The clustering accuracy on three datasets is 83.60%, 63.65%, and 86.03%, the FLOPs are 3.57G, 30.62G, and 2.95G. The source code will be available at https://github.com/DY-HYX.

Unsupervised domain adaptation (UDA) techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data to learn domain invariant features for cross-scene classification. Compared to natural images, numerous spectral bands of HSIs provide abundant semantic information, but they also increase the domain shift significantly. In most existing methods, both explicit alignment and implicit alignment simply align feature distribution, ignoring domain information in the spectrum. We noted that when the spectral channel between source and target domains is distinguished obviously, the transfer performance of these methods tends to deteriorate. Additionally, their performance fluctuates greatly owing to the varying domain shifts across various datasets. To address these problems, a novel shift-sensitive spatial-spectral disentangling learning (S⁴DL) approach is proposed. In S⁴DL, gradient-guided spatial-spectral decomposition (GSSD) is designed to separate domain-specific and domain-invariant representations by generating tailored masks under the guidance of the gradient from domain classification. A shift-sensitive adaptive monitor is defined to adjust the intensity of disentangling according to the magnitude of domain shift. Furthermore, a reversible neural network is constructed to retain domain information that lies not only in semantic but also the shallow-level detailed information. Extensive experimental results on several cross-scene HSI datasets consistently verified that S⁴DL is better than the state-of-the-art UDA methods. Our source code will be available at https://github.com/xdu-jjgs/IEEE_TNNLS_S4DL.