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.

Recently, the Transformer-based model has shown superior performance in hyperspectral image classification (HSIC) due to its excellent ability to model long-term dependencies on sequence data. An important component of the Transformer is the tokenizer, which can transform the features into semantic token sequences (STS). Nonetheless, Transformer's semantic tokenization strategy is hardly representative of local relatively important high-level semantics because of its global receptive field. Recently, the Mamba-based methods have shown even stronger spatial context modeling ability than Transformer for HSIC. However, these Mamba-based methods mainly focus on spectral and spatial dimensions. They tend to extract semantic information in very long feature sequences or represent semantic information in several typical tokens, which may ignore some important semantics of the HSIs. In order to represent the semantic information of HSIs more holistically in Mamba, this article proposes a semantic tokenization-based Mamba (STMamba) model. In STMamba, a spectral-spatial feature extraction (SSFE) module is used to extract the spectral-spatial joint features. Then a generated semantic token sequences module (GSTSM) is designed to transform the features into STS. Subsequently, the STS are fed into the semantic token state spatial model (STSSM) to capture relationships between different semantic tokens. Finally, the fused semantic token is passed into a classifier for classification. Experimental results on three HSI datasets demonstrate that the proposed STMamba outperforms existing state-of-the-art deep learning and transformer-based methods. The source code can be found at https://github.com/AlanLowell/STMamba.

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.

Hyperspectral image classification (HSI) has been widely used in many fields. However, image noise, atmospheric conditions, material distribution and other factors seriously degrade the classification accuracy of HSIs. To alleviate these issues, a new approach, namely adaptive weighted quaternion Zernike moments (AWQZM), is proposed, which extracts effective spatial-spectral features for pixels in HSI classification. The main contributions and novelties of the method are as follows: 1) the AWQZM can adaptively set weights for each pixel in the neighborhood, which not only can flexibly search for homogeneous regions of HSIs, but also can strengthen the similarity of pixels from the same class and the distinctiveness of pixels from different classes; 2) the AWQZM can be constructed in a small subset of bands through a grouping strategy, thereby reducing the computational complexity; and 3) the introduction of quaternions can preserve the spatial correlation among bands and reduce the loss of data information, and the use of quaternion phase information makes the extracted features more informative and discriminative. Moreover, the spectral features and spatial features are combined to achieve better HSI classification results. Experimental results on three benchmark data sets demonstrate that the proposed approach achieves better classification performance than other related approaches.

This paper proposes a framework for hyperspectral images (HSIs) classification with composite kernels discriminant analysis (CKDA). The CKDA uses the spectral and spatial information extracted by Gaussian weighted local mean operator (GWLM) and is suitable to solve few labeled samples classification problem of HSI, which has very important practical significance for the case that training samples are insufficient due to high cost. Experimental results show that the spatial information extracted by GWLM can greatly improve the performance, and demonstrate the superiority of CKDA for HSI classification in the case of few labeled samples. Compared with other state-of-the-art spectral-spatial kernel methods, the proposed methods also show very good advantages, especially the parallel kernel method.

The functional principal component analysis (FPCA) method can effectively solve the problems of the high dimensionality of data, large information redundancy, and noise interference in hyperspectral image (HSI) classification. However, this unsupervised FPCA cannot make full use of the label information of training samples or spatial information, so that it is impossible to obtain satisfactory classification results. In this letter, a set of improved FPCA methods for HSI classification are proposed. First, the B-spline basis system is used to establish the functional data fitting model, which can convert discrete spectral information into continuous spectral curves and lay the foundation for functional feature extraction. Second, a supervised FPCA (SFPCA) method is built for extracting more effective functional features by making full use of the label information of training samples. Furthermore, to overcome the lack of training samples, two semisupervised FPCA (SSFPCA) methods are proposed for extracting more discriminative functional features and improving the classification accuracies. Finally, we perform the local mean filtering method on the HSI in order to extract the spatial information for each pixel, and then design spectral-spatial classification frameworks based on improved FPCA. Experiments on the commonly used HSI dataset show that improved FPCA can achieve higher classification accuracies than FPCA, and the proposed functional spectral-spatial classification frameworks can greatly improve classification accuracies.