Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer’s multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance.

In recent years, deep learning has shown promising results in change detection (CD) of hyperspectral images (HSIs). However, these algorithms often require a large number of labeled samples, which can be costly in practical applications. This paper proposes a few-shot HSI CD method based on 3D convolution neural network and normalizing flow. To mitigate the issue of limited training samples, we develop a low-parameter baseline CD model utilizing 3D convolution neural networks to extract spectral-spatial features while preventing overfitting. Additionally, the proposed method analyzes the spectral-spatial distribution of hyperspectral variation by normalizing flow and generates hyperspectral tensor samples that approximate the distribution, thus enhancing CD performance. Furthermore, to improve the sample generation speed, we employ soft labels and label smoothing techniques to assign high-quality labels to the generated samples, thereby increasing the number of available samples. The method is evaluated on three datasets, and the experimental results demonstrate the efficacy of the proposed approach for detecting changes with limited training samples. The proposed method offers a promising solution to the challenge of detecting changes with limited training samples in HSIs.

Hyperspectral image (HSI) target detection is an advanced technology within the remote sensing society, widely applied in both civilian and military domains. However, subpixel target detection poses a significantly challenging problem in HSI processing. Existing methods for HSI target detection mostly rely on constructing detectors based on linear mixing model (LMM). Nevertheless, due to multiple scattering, LMM fails to capture the nonlinear features and extensive interactions among various materials commonly present in real HSIs, potentially resulting in poor performance. To address this issue, this work proposes a novel method for HTD based on bilinear mixing model (BMM) in HSI. Specifically, we designed a spectral decomposition network to separate the background and target. To further extract and combine spectral and spatial information of the image for more accurate learning of target and background distributions, a deep network model based on a multiscale feature extraction module is proposed. Finally, to enhance the discrimination between targets and backgrounds, a customized CEM loss is formulated by minimizing the response of the background and maximizing the response of the target. Experimental comparisons on six real HSI datasets validate the superiority of the proposed detector.