{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T03:05:25Z","timestamp":1763348725655,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62401203","2023NK2011","2023NK2002","2023JJ40333"],"award-info":[{"award-number":["62401203","2023NK2011","2023NK2002","2023JJ40333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provincial Key Research and Development Program","award":["62401203","2023NK2011","2023NK2002","2023JJ40333"],"award-info":[{"award-number":["62401203","2023NK2011","2023NK2002","2023JJ40333"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["62401203","2023NK2011","2023NK2002","2023JJ40333"],"award-info":[{"award-number":["62401203","2023NK2011","2023NK2002","2023JJ40333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Hyperspectral image (HSI) change detection (CD) is an important technology for identifying surface changes using multi-temporal HSIs. Nevertheless, the high dimensionality of HSIs presents significant challenges for CD tasks, including issues such as lack of robustness and high computational costs in existing methods. To address those issues, this paper proposes an unsupervised simple and effective HSI CD model termed L2,1-norm regularized double non-negative matrix factorization (L2,1-DNMF). Specifically, the proposed model employs a symmetric double non-negative matrix factorization (NMF) framework to jointly analyze multitemporal HSIs, capturing their common and invariant structural information to construct a shared feature basis. Meanwhile, two non-negative feature weight matrices are learned to generate a differential image matrix that directly reflects the change regions. To enhance robustness against noise, an L2,1-norm constraint is imposed on the difference image matrix, ensuring that unchanged areas exhibit near-zero values while changed areas present nonzero values. Finally, comprehensive experiments performed on three benchmark hyperspectral datasets validated the efficacy of the proposed method, which is superior to some state-of-the-art ones regarding detection performance and computational cost.<\/jats:p>","DOI":"10.3390\/sym17020304","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T07:48:22Z","timestamp":1739778502000},"page":"304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["L2,1-Norm Regularized Double Non-Negative Matrix Factorization for Hyperspectral Change Detection"],"prefix":"10.3390","volume":"17","author":[{"given":"Xing-Hui","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng-Ting","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2532-1567","authenticated-orcid":false,"given":"Yang-Jun","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu-Ming","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-4704","authenticated-orcid":false,"given":"Chen-Feng","family":"Long","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2024.12.010","article-title":"Refined change detection in heterogeneous low-resolution remote sensing images for disaster emergency response","volume":"220","author":"Wang","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6501","DOI":"10.1109\/JSTARS.2024.3373401","article-title":"Similarity learning for land use scene-level change detection","volume":"17","author":"Liu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","first-page":"104077","article-title":"Large kernel convolution application for land cover change detection of remote sensing images","volume":"132","author":"Huang","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1109\/TPAMI.2024.3475824","article-title":"Changen2: Multi-temporal remote sensing generative change foundation model","volume":"47","author":"Zheng","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2501805","DOI":"10.1109\/LGRS.2023.3267879","article-title":"Multiscale attention network guided with change gradient image for land cover change detection using remote sensing images","volume":"20","author":"Lv","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6002605","DOI":"10.1109\/LGRS.2023.3247882","article-title":"Iterative edge enhancing framework for building change detection","volume":"21","author":"Song","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","first-page":"4621","article-title":"FIBTNet: Building change detection for remote sensing images using feature interactive bi-temporal network","volume":"80","author":"Wang","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_8","first-page":"103836","article-title":"CDasXORNet: Change detection of buildings from bi-temporal remote sensing images as an XOR problem","volume":"130","author":"Chen","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112636","DOI":"10.1016\/j.rse.2021.112636","article-title":"Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters","volume":"265","author":"Zheng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5919","DOI":"10.1109\/JSTARS.2023.3285389","article-title":"Data augmentation and few-shot change detection in forest remote sensing","volume":"16","author":"Zhu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s11431-021-1989-9","article-title":"Self-supervised learning-based oil spill detection of hyperspectral images","volume":"65","author":"Duan","year":"2022","journal-title":"Sci. China Technol. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112741","DOI":"10.1016\/j.rse.2021.112741","article-title":"Multi-sensor change detection for within-year capture and labelling of forest disturbance","volume":"268","author":"Cardille","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5618214","DOI":"10.1109\/TGRS.2023.3305499","article-title":"Adjacent-level feature cross-fusion with 3-D CNN for remote sensing image change detection","volume":"61","author":"Ye","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1109\/TNNLS.2023.3347301","article-title":"Cycle-refined multidecision joint alignment network for unsupervised domain adaptive hyperspectral change detection","volume":"36","author":"Qu","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wen, X., and Yang, X. (2009, January 18\u201319). Change detection from remote sensing imageries using spectral change vector analysis. Proceedings of the 2009 Asia-Pacific Conference on Information Processing, Shenzhen, China.","DOI":"10.1109\/APCIP.2009.183"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2011.2171493","article-title":"A framework for automatic and unsupervised detection of multiple changes in multitemporal images","volume":"50","author":"Bovolo","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TGRS.2015.2396686","article-title":"Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images","volume":"53","author":"Liu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(97)00162-4","article-title":"Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal Image data: New approaches to change detection studies","volume":"64","author":"Nielsen","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TIP.2006.888195","article-title":"The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data","volume":"16","author":"Nielsen","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TGRS.2013.2266673","article-title":"Slow feature analysis for change detection in multispectral imagery","volume":"52","author":"Wu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2017.07.009","article-title":"A post-classification change detection method based on iterative slow feature analysis and bayesian soft fusion","volume":"199","author":"Wu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, B., Niu, Y., Xia, W., Zhang, J.Q., and Hu, B. (2015, January 26\u201331). Change detection for hyperspectral images based on tensor analysis. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326105"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hou, Z., Li, W., and Du, Q. (2021, January 11\u201316). A patch tensor-cased change detection method for hyperspectral images. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554630"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.09.005","article-title":"Hyperspectral anomalous change detection based on joint sparse representation","volume":"146","author":"Wu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","unstructured":"Du, Q., Wasson, L., and King, R. (2005, January 16\u201318). Unsupervised linear unmixing for change detection in multitemporal airborne hyperspectral imagery. Proceedings of the International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Biloxi, MS, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/JSTARS.2015.2477431","article-title":"Sparse unmixing-based change detection for multitemporal hyperspectral images","volume":"9","author":"Iordache","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/JSTARS.2016.2606514","article-title":"Sparse unmixing with dictionary pruning for hyperspectral change detection","volume":"10","author":"Iordache","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ert\u00fcrk, A. (2020, January 9\u201311). Constrained nonnegative matrix factorization for hyperspectral change detection. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105146"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5501513","DOI":"10.1109\/TGRS.2023.3235401","article-title":"Abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention for hyperspectral change detection","volume":"61","author":"Dong","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9976","DOI":"10.1109\/TGRS.2019.2930682","article-title":"Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images","volume":"57","author":"Du","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4404912","DOI":"10.1109\/TGRS.2024.3378526","article-title":"Novel distribution distance based on inconsistent adaptive region for change detection using hyperspectral remote sensing images","volume":"62","author":"Lv","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5501214","DOI":"10.1109\/TGRS.2023.3341893","article-title":"Feature mutual representation-based graph domain adaptive network for unsupervised hyperspectral change detection","volume":"62","author":"Qu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"103663","article-title":"Progressive pseudo-label framework for unsupervised hyperspectral change detection","volume":"127","author":"Li","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, W., and Shi, X. (2024). A Gradient-Based Algorithm with Nonmonotone Line Search for Nonnegative Matrix Factorization. Symmetry, 16.","DOI":"10.3390\/sym16020154"},{"key":"ref_37","unstructured":"Lee, D.D., and Seung, H.S. (2000, January 1). Algorithms for non-negative matrix factorization. Proceedings of the 14th International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5509314","DOI":"10.1109\/TGRS.2024.3363159","article-title":"Feature Dimensionality Reduction with L2,p-Norm-Based Robust Embedding Regression for Classification of Hyperspectral Images","volume":"62","author":"Deng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1137\/080730421","article-title":"A fast algorithm for edge-preserving variational multichannel image restoration","volume":"2","author":"Yang","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/j.patcog.2007.09.010","article-title":"SVD based initialization: A head start for nonnegative matrix factorization","volume":"41","author":"Boutsidis","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1109\/TGRS.2012.2228210","article-title":"Assessment of spectral band impact on intercalibration over desert sites using simulation based on EO-1 hyperion data","volume":"51","author":"Henry","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5507312","DOI":"10.1109\/TGRS.2021.3090802","article-title":"Hyperspectral change detection based on multiple morphological profiles","volume":"60","author":"Hou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/304\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:36:10Z","timestamp":1760027770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["sym17020304"],"URL":"https:\/\/doi.org\/10.3390\/sym17020304","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,2,17]]}}}