Hyperspectral anomaly detection(HAD) aims to identify anomalous targets that differ from the background in high-dimensional spectral images, and is widely applied in fields such as military reconnaissance and environmental monitoring. However, the diversity of anomaly scales, interference from complex backgrounds, and redundancy of spectral information pose significant challenges to achieving high detection accuracy. To address these issues, this letter proposes a Multi-scale Attention-guided Context Network (MACNet) to enhance the perception of anomalous regions. MACNet consists of three components: a Multi-Scale Local Feature Extractor (MSLFE) that effectively captures edge structures and subtle anomalies at different scales, a Global Context Awareness Module (GCAM) that fuses local and global contextual information to improve discrimination under complex backgrounds, and a Refined Reconstruction and Contrast Enhancement module (RRCE) that employs channel attention and spatial reconstruction mechanisms to enhance the response differences between anomalies and background. Experiments on four publicly available hyperspectral datasets demonstrate that MACNet achieves superior detection accuracy compared to existing mainstream methods, validating the effectiveness of the proposed approach.

Hyperspectral image (HSI) classification faces critical challenges in effectively modeling the intricate spectral-spatial structures and non-Euclidean relationships. Traditional methods often struggle to simultaneously capture local details, global contextual dependencies, and graph-structured correlations, leading to limited classification accuracy. To address the above issues, this letter proposes a Graph-Aware Hybrid Encoding (GAHE) framework. To fully exploit the spectral-spatial characteristics and graph structural dependencies inherent in HSI, the proposed method is structured into three key components: a multi-scale selective graph-aware attention module, a hybrid projection encoding module, and a graph sensitive aggregation module. The three modules work in a complementary manner to progressively refine and enhance feature representations across multiple scales and modalities. Comparing with advanced classification methods,the experimental results demonstrate that the proposed GAHE method shows better classification performance.

Deep learning has shown great promise in hyperspectral unmixing (HU), especially the unmixing methods based on autoencoder (AE) networks, which are the most prevalent these days. Since most spectral mixing problems are nonlinear and cannot effectively utilize global context information, these methods have limited generalization ability under different ground features and scenarios. To address these limitations, an enhanced network based on spatial-spectral information attention is proposed. A two-channel attention mechanism is embedded within the convolutional AE to acquire the feature dependencies. The spatial information extraction uses the dynamic large kernel block (DLK) to obtain the global spatial attention of the image. The DLK module uses multiple large kernels with different kernel sizes and dilation rates to capture multiscale features, and the spectral information extraction uses coordinate attention (CA) to capture spectral correlation information. This can improve the quality of the endmember spectra and abundance maps. On real and synthetic data, this model is compared with several advanced unmixing methods, and the results show the effectiveness of this method.

Spatial resolution enhancement and its subsequent tasks are always separated in conventional hyperspectral image (HSI) processing model. The requirement of the following task, such as unmixing, cannot be referred by spatial resolution enhancement. Moreover, errors and artifacts will also be transmitted and accumulated. In this work, we propose a joint processing method of spatial resolution enhancement and spectral unmixing for HSI (J-SRE-Un), where these two tasks are treated as constraints for each other to simultaneously achieve better performance. Experiments on both simulated and real data demonstrate the effectiveness and superiority of our method.

The curse of dimensionality in high-dimensional data makes it difficult to capture the abnormality of data points in full data space. To deal with this problem, we propose an outlier detection model based on Variational Autoencoder and Genetic Algorithm for subspace outlier analysis of high-dimensional data (VAGA). The proposed VAGA model constructs a variational autoencoder (VAE) to preliminarily detect outliers. Then the genetic algorithm (GA) is used to search the abnormal subspace of the outliers obtained by the VAE layer to provide a basis for subspace outlier analysis. The subsequent clustering of the abnormal subspaces help filter out the false positives which are fed back to the VAE layer to adjust network weights. The comparative experiments performed on three public benchmark datasets show that the outlier detection results of the proposed VAGA model are highly interpretable and have better accuracy performance than the state-of-the-art outlier detection methods.

Hyperspectral images are mixtures of spectra of materials in a scene. Accurate analysis of hyperspectral image requires spectral unmixing. The result of spectral unmixing is the material spectral signatures and their corresponding fractions. The materials are called endmembers. Endmember extraction equals to acquire spectral signatures of the materials. In this study, the authors propose a new hyperspectral endmember extraction algorithm for hyperspectral image based on QR factorisation using Givens rotations (EEGR). Evaluation of the algorithm is demonstrated by comparing its performance with two popular endmember extraction methods, which are vertex component analysis (VCA) and maximum volume by householder transformation (MVHT). Both simulated mixtures and real hyperspectral image are applied to the three algorithms, and the quantitative analysis of them is presented. EEGR exhibits better performance than VCA and MVHT. Moreover, EEGR algorithm is convenient to implement parallel computing for real‐time applications based on the hardware features of Givens rotations.