{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T18:04:01Z","timestamp":1784570641861,"version":"3.55.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund of Education Department of Hunan Province","award":["24C0318"],"award-info":[{"award-number":["24C0318"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Change detection (CD) can detect changes in the earth\u2019s surface. However, the existing Siamese network-based CD methods usually calculate the Euclidean distance (ED) between two branches to obtain difference features, ignoring the deeper feature disparities captured by the CNN. In addition, the patch obtained in the CD method using spatial information may have the phenomenon that the adjacent pixels and the pixels to be classified are not the same object or change type. To deal with the aforementioned challenges, we propose FDS-BINet, a multi-spatial resolution network integrating a Feature Difference Structure (FDS) and Bicubic Interpolation (BI). The FDS module progressively extracts deep pixel-level differences through residual-enhanced Siamese branches, while BI interpolates the absolute distance spectrum to recover subpixel-level changes. The main contributions of this article are as follows: 1) A change detection framework called FDS-BINet has been proposed that fuses pixel-level and subpixel-level change information. 2) A feature difference structure (FDS) is designed to obtain deep difference features of pixel-level spatial information of bitemporal HSIs; 3) The bicubic interpolation (BI) algorithm is used for absolute distance (AD) spectrum interpolation. We can effectively boost the spatial resolution of AD spectrum data, enabling us to extract feature differences at the subpixel level from bitemporal hyperspectral images (HSIs). The experimental results demonstrate that the proposed FDS-BINet has better performance compared with representative methods in the field of HSI CD.<\/jats:p>","DOI":"10.1371\/journal.pone.0353888","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:31:27Z","timestamp":1784568687000},"page":"e0353888","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-spatial resolution hyperspectral image change detection network integrating feature difference structure"],"prefix":"10.1371","volume":"21","author":[{"given":"Yanhua","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuixiang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafei","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4616-2245","authenticated-orcid":true,"given":"Huayan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"issue":"02","key":"pone.0353888.ref001","doi-asserted-by":"crossref","DOI":"10.1117\/1.JRS.15.028505","article-title":"Hyperspectral change detection based on modification of UNet neural networks","volume":"15","author":"MS Moustafa","year":"2021","journal-title":"J Appl Rem Sens"},{"issue":"2","key":"pone.0353888.ref002","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MGRS.2019.2898520","article-title":"A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges","volume":"7","author":"S Liu","year":"2019","journal-title":"IEEE Geosci Remote Sens Mag"},{"issue":"4","key":"pone.0353888.ref003","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1109\/TGRS.2003.810712","article-title":"Automatic reduction of hyperspectral imagery using wavelet spectral analysis","volume":"41","author":"S Kaewpijit","year":"2003","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3156041","article-title":"A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image Change Detection","volume":"60","author":"X Ou","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"4","key":"pone.0353888.ref005","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1080\/01431161.2015.1007252","article-title":"NDVI-based land-cover change detection using harmonic analysis","volume":"36","author":"M Jung","year":"2015","journal-title":"International Journal of Remote Sensing"},{"key":"pone.0353888.ref006","unstructured":"Malila WA. Change vector analysis: An approach for detecting forest changes with Landsat. LARS symposia. 1980. 385."},{"issue":"4","key":"pone.0353888.ref007","doi-asserted-by":"crossref","first-page":"369","DOI":"10.14358\/PERS.69.4.369","article-title":"Land-Use\/Land-Cover Change Detection Using Improved Change-Vector Analysis","volume":"69","author":"J Chen","year":"2003","journal-title":"photogramm eng remote sensing"},{"issue":"16","key":"pone.0353888.ref008","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA\u2010based land\u2010use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"JS Deng","year":"2008","journal-title":"International Journal of Remote Sensing"},{"issue":"1","key":"pone.0353888.ref009","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":"AA Nielsen","year":"1998","journal-title":"Remote Sensing of Environment"},{"issue":"5","key":"pone.0353888.ref010","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":"C Wu","year":"2014","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref011","doi-asserted-by":"crossref","first-page":"132726","DOI":"10.1016\/j.neucom.2026.132726","article-title":"A lightweight framework for robust object detection in adverse weather based on dual-teacher feature alignment","volume":"671","author":"R Hu","year":"2026","journal-title":"Neurocomputing"},{"issue":"2","key":"pone.0353888.ref012","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/15481603.2018.1430100","article-title":"Object-wise joint-classification change detection for remote sensing images based on entropy query-by fuzzy ARTMAP","volume":"55","author":"M Han","year":"2018","journal-title":"GIScience & Remote Sensing"},{"key":"pone.0353888.ref013","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":"C Wu","year":"2017","journal-title":"Remote Sensing of Environment"},{"issue":"2","key":"pone.0353888.ref014","article-title":"SSA: semantic structure aware inference on CNN networks for weakly pixel-wise dense predictions without cost","volume":"19","author":"Y Sun","year":"2024","journal-title":"Front Comput Sci"},{"issue":"5","key":"pone.0353888.ref015","article-title":"Graph-Segmenter: graph transformer with boundary-aware attention for semantic segmentation","volume":"18","author":"Z Wu","year":"2023","journal-title":"Front Comput Sci"},{"issue":"4","key":"pone.0353888.ref016","article-title":"Soft-GNN: towards robust graph neural networks via self-adaptive data utilization","volume":"19","author":"Y Wu","year":"2024","journal-title":"Front Comput Sci"},{"key":"pone.0353888.ref017","first-page":"1","article-title":"BS3LNet: A New Blind-Spot Self-Supervised Learning Network for Hyperspectral Anomaly Detection","volume":"61","author":"L Gao","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3241097","article-title":"Multiscale Diff-Changed Feature Fusion Network for Hyperspectral Image Change Detection","volume":"61","author":"F Luo","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref019","first-page":"1","article-title":"Hyperspectral Image Change Detection Based on Gated Spectral\u2013Spatial\u2013Temporal Attention Network With Spectral Similarity Filtering","volume":"62","author":"H Yu","year":"2024","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3379199","article-title":"Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image Denoising","volume":"62","author":"L Zhuang","year":"2024","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref021","doi-asserted-by":"crossref","first-page":"122125","DOI":"10.1016\/j.eswa.2023.122125","article-title":"A dual-branch siamese spatial-spectral transformer attention network for Hyperspectral Image Change Detection","volume":"238","author":"Y Zhang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"pone.0353888.ref022","doi-asserted-by":"crossref","first-page":"6426","DOI":"10.1109\/JSTARS.2024.3370151","article-title":"Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection","volume":"17","author":"Y Huang","year":"2024","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"9","key":"pone.0353888.ref023","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.3390\/s25092813","article-title":"Semantic-Aware Remote Sensing Change Detection with Multi-Scale Cross-Attention","volume":"25","author":"X Zheng","year":"2025","journal-title":"Sensors (Basel)"},{"key":"pone.0353888.ref024","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.isprsjprs.2023.07.001","article-title":"An attention-based multiscale transformer network for remote sensing image change detection","volume":"202","author":"W Liu","year":"2023","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"pone.0353888.ref025","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1109\/JSTARS.2023.3264802","article-title":"HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images","volume":"16","author":"C Han","year":"2023","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"pone.0353888.ref026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3139077","article-title":"A Spectral and Spatial Attention Network for Change Detection in Hyperspectral Images","volume":"60","author":"M Gong","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"10","key":"pone.0353888.ref027","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/LGRS.2019.2953754","article-title":"Multispectral Change Detection With Bilinear Convolutional Neural Networks","volume":"17","author":"Y Lin","year":"2020","journal-title":"IEEE Geosci Remote Sensing Lett"},{"key":"pone.0353888.ref028","first-page":"1","article-title":"SSA-SiamNet: Spectral\u2013Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection","volume":"60","author":"L Wang","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref029","doi-asserted-by":"crossref","first-page":"7724","DOI":"10.1109\/JSTARS.2022.3204541","article-title":"A Hyperspectral Image Change Detection Framework With Self-Supervised Contrastive Learning Pretrained Model","volume":"15","author":"X Ou","year":"2022","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"issue":"3","key":"pone.0353888.ref030","article-title":"Robust domain adaptation with noisy and shifted label distribution","volume":"19","author":"S-Y Li","year":"2024","journal-title":"Front Comput Sci"},{"key":"pone.0353888.ref031","first-page":"1","article-title":"RSCNet: A Residual Self-Calibrated Network for Hyperspectral Image Change Detection","volume":"60","author":"L Wang","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"8","key":"pone.0353888.ref032","doi-asserted-by":"crossref","first-page":"9774","DOI":"10.1109\/TPAMI.2023.3237896","article-title":"Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection","volume":"45","author":"C Wu","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"pone.0353888.ref033","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"R Keys","year":"1981","journal-title":"IEEE Trans Acoust, Speech, Signal Process"},{"issue":"2","key":"pone.0353888.ref034","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/JSTARS.2013.2241396","article-title":"A Subspace-Based Change Detection Method for Hyperspectral Images","volume":"6","author":"C Wu","year":"2013","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"issue":"1","key":"pone.0353888.ref035","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Q Wang","year":"2019","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref036","unstructured":"L\u00f3pez-Fandi\u00f1o J, Garea AS, Heras DB, Arg\u00fcello F. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018. 1906\u20139."},{"key":"pone.0353888.ref037","first-page":"1","article-title":"Spectral\u2013Spatial\u2013Temporal Transformers for Hyperspectral Image Change Detection","volume":"60","author":"Y Wang","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref038","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3216878","article-title":"CDFormer: A Hyperspectral Image Change Detection Method Based on Transformer Encoders","volume":"19","author":"J Ding","year":"2022","journal-title":"IEEE Geosci Remote Sensing Lett"},{"key":"pone.0353888.ref039","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3263563","article-title":"CBW-MSSANet: A CNN Framework With Compact Band Weighting and Multiscale Spatial Attention for Hyperspectral Image Change Detection","volume":"61","author":"X Ou","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0353888.ref040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3469930","article-title":"DIEFEN: Differential Information-Enhanced Feature Exchange Network for Hyperspectral Change Detection","volume":"62","author":"L Wu","year":"2024","journal-title":"IEEE Trans Geosci Remote Sensing"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:31:35Z","timestamp":1784568695000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353888"}},"subtitle":[],"editor":[{"given":"Longxiu","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,20]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0353888","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,20]]}}}