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The transformer has been introduced in the hyperspectral image (HSI) classification, demonstrating outstanding capability in capturing global features compared to the convolutional neural network (CNN). However, the small-sample-size problem poses a significant challenge in practical HSI classification, especially in training the transformer. To tackle this issue, cross-domain transfer learning is adopted as a practical solution, which transfers the information from a source domain with abundant labeled samples to a target domain with limited labeled samples. This article proposes a novel transfer learning method for heterogeneous cross-domain HSI classification called cross-domain discriminative vision transformer (CD-DViT). This algorithm primarily contains three key contributions. First, source samples are mapped to the target domain through an encoder-decoder architecture, and the mapped source samples can be used to train the target classifier. Second, the cross-attention mechanism is utilized to construct two blocks for achieving the domainwise and classwise feature alignments (FAs), respectively. Specifically, the combination of the cross-attention mechanism with the domain discriminator aims to learn domain-invariant features, thereby facilitating domainwise alignment and alleviating domain shift. Third, knowledge distillation (KD) is adopted to learn more information from the target domain and assist in classifying target samples. Our experiments on three real-world cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.

Hyperspectral image (HSI) denoising is a crucial preprocessing step for subsequent tasks. The clean HSI usually reside in a low-dimensional subspace, which can be captured by low-rank and sparse representation, known as the physical prior of HSI. It is generally challenging to adequately use such physical properties for effective denoising while preserving image details. This article introduces a novel iterative low-rank network (ILRNet) to address these challenges. ILRNet integrates the strengths of model-driven and data-driven approaches by embedding a rank minimization module (RMM) within a U-Net architecture. This module transforms feature maps into the wavelet domain and applies singular value thresholding (SVT) to the low-frequency components during the forward pass, leveraging the spectral low-rankness of HSIs in the feature domain. The parameter, closely related to the hyperparameter of the singular vector thresholding algorithm, is adaptively learned from the data, allowing for flexible and effective capture of low-rankness across different scenarios. Additionally, ILRNet features an iterative refinement process that adaptively combines intermediate denoised HSIs with noisy inputs. This manner ensures progressive enhancement and superior preservation of image details. Experimental results demonstrate that ILRNet achieves state-of-the-art performance in both synthetic and real-world noise removal tasks.

Change detection is a crucial technique in remote sensing image analysis and faces challenges, such as background complexity and appearance shift, resulting in incomplete change boundaries and pseudochanges. This article introduces a novel wavelet Siamese network with semi-supervised domain adaptation (DA) to address these issues, named WS-Net++. WS-Net++ establishes spatial–frequency interactions between bitemporal images to enhance the completeness of the change boundaries. The spatial-domain interaction highlights the pixelwise differences. The frequency-domain interaction first adaptively adjusts the contributions from different frequency components based on image context. Within-frequency and between-frequency interactions are further constructed to capture the frequency-domain differences, enabling the adaptive and effective handling of both overall and subtle changes. In addition, WS-Net++ employs a semi-supervised DA strategy to mitigate the appearance shifts between bitemporal images. By categorizing regions into changed, unchanged, and regions of no interest in a semi-supervised manner, the network minimizes intraclass discrepancies within unchanged regions and maximizes interclass discrepancies between changed regions, reducing the domain gap. Experimental results on the LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that our WS-Net++ outperforms alternative methods, achieving the $F1$ scores of 91.31%, 94.52%, and 79.77%, respectively. The code and models will be publicly available at https://github.com/JiTaiTai/WS-Net_Plus for reproducible research.

Sparse representation (SR)-based hyperspectral image (HSI) denoising methods normally average the local denoising results of multiple overlapped cubes to recover the whole HSI. Though interpretable, they rely on cumbersome hyperparameter settings and ignore the relationship between overlapped cubes, leading to poor denoising performance. This article combines SR and convolutional neural networks and introduces a deep parameterized sparse neural network (DPNet-S) to address the above issues. DPNet-S parameterizes the SR-based HSI denoising model with two modules: 1) sparse optimizer to extract sparse feature maps from noisy HSIs via recurrent usage of convolution, deconvolution, and soft shrinkage operations; and 2) image reconstructor to recover the denoised HSI from its sparse feature maps via deconvolution operations. We further replace the soft shrinkage operator with U-Net architecture to account for general HSI priors and more effectively capture the complex structures of HSIs, resulting in DPNet-U. Both networks directly learn the parameters from data and perform denoising on the whole HSI, which overcomes the limitations of SR-based methods. Moreover, our networks are generated from the denoising model and optimization procedures, thus leveraging the knowledge embedded and relying less on the number of training samples. Extensive experiments on both synthetic and real-world HSIs show that our DPNet-S and DPNet-U achieve remarkable results when compared with state-of-the-art methods. The codes will be publicly available at https://github.com/bearshng/dpnets for reproducible research.