{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:25:56Z","timestamp":1761582356067,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sea Sense project"},{"name":"Net Zero Technology Centre, UK"},{"name":"Robert Gordon University PhD scholarship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth\u2019s surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial\u2013spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models.<\/jats:p>","DOI":"10.3390\/rs16132353","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T08:57:50Z","timestamp":1719478670000},"page":"2353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SSA-LHCD: A Singular Spectrum Analysis-Driven Lightweight Network with 2-D Self-Attention for Hyperspectral Change Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Yinhe","family":"Li","sequence":"first","affiliation":[{"name":"National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[{"name":"National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK"}]},{"given":"Yijun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK"}]},{"given":"Genyun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Ping","family":"Ma","sequence":"additional","affiliation":[{"name":"National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"ref_1","first-page":"5505405","article-title":"PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral Classification","volume":"20","author":"Yan","year":"2021","journal-title":"IEEE Geosci. 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