Adaptive Center-Focused Hybrid Attention Network for Change Detection in Hyperspectral Images

作     者：Jiang, Fenlong Zhang, Shining Zhang, Mingyang Gong, Maoguo Zhou, Yu Zhao, Wei Guan, Ziyu 

作者机构：Xidian Univ Sch Comp Sci & Technol Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Inner Mongolia Normal Univ Acad Artificial Intelligence Coll Math Sci Hohhot 010011 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 (IEEE Trans Geosci Remote Sens)

年 卷 期：2025年第63卷

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Natural Science Foundation of China [62036006, 62376205, 62133012, 61936006, 62425605] Key Research and Development Program of Shaanxi [2024CY2-GJHX-15] Natural Science Basic Research Program of Shaanxi [2024JC-YBQN-0633] Fundamental Research Funds for the Central Universities, China [XJSJ24016] 

主　　题：Feature extraction Accuracy Costs Vectors Redundancy Labeling Correlation Hyperspectral imaging Data mining Computational modeling Center-focused self-attention change detection (CD) hyperspectral images (HSIs) superpixel segmentation 

摘      要：Hyperspectral images (HSIs) capture extensive spatial and spectral information, facilitating detailed change detection (CD) of complex land covers. However, the high correlation among spectral data can lead to information redundancy, increasing processing dimensions and introducing irrelevant or detrimental data to CD. To address these challenges, we propose an adaptive center-focused hybrid attention network (ACFHAN) for CD in HSIs. This network adaptively emphasizes the spatial regions and spectral channels most pertinent to CD while suppressing irrelevant information. The architecture establishes an end-to-end mapping from the two HSIs to the change results, featuring multiple center-focused hybrid attention blocks (CFHABs). Each CFHAB integrates two different attention modules, including an adaptive spatial-spectral hybrid self-attention (S2HSA) module that dynamically adjusts spatial-spectral feature weights and a center-focused attention (CFA) module that enhances the area most relevant to the center pixel to be classified. Additionally, to tackle the challenges of expensive labeling, we further designed a multiscale superpixel-based data augmentation method which combines traditional unsupervised and supervised methods to provide sufficient low-cost but high-confidence labeled data for CD. Experimental results across various HSI CD datasets validate the effectiveness of our proposed method.