{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T23:44:29Z","timestamp":1776987869635,"version":"3.51.4"},"reference-count":72,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"vor","delay-in-days":275,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Transformer and convolutional neural network (CNN) have made significant progress in the issue of remote sensing binary change detection. However, Transformer has high quadratic computational complexity, while CNN is limited by a fixed receptive field, which may hinder their capability of learning spatial contextual features. Inspired by the remarkable performance of Mamba on the task of natural language processing, which can effectively make up for the deficiencies of the above two architectures, we tailor the structure of Mamba to solve the issue of binary change detection. In this work, we explore the potential of visual Mamba to address the task of binary change detection in remote sensing imageries, which is abbreviated as Mam\u2010BCD. The entire network is designed as an encoder\u2013decoder architecture. The encoder employs the effective visual Mamba to fully learn global spatial contextual features from input images. For the decoder, we introduce three spatio\u2010temporal feature learning strategies, which can be organically integrated into the Mamba architecture to achieve spatio\u2010temporal interaction between different temporal features. Comprehensive experiments are conducted on three public available datasets to verify the efficacy of the proposed Mam\u2010BCD. Compared to the advanced CTDFormer, Mam\u2010BCD achieves 4.49%, 8.73% and 3.44% gain in accuracy metric on SYSU\u2010CD, LEVIR\u2010CD+ and WHU\u2010CD datasets,\u00a0respectively.<\/jats:p>","DOI":"10.1049\/ipr2.70214","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T14:24:32Z","timestamp":1759501472000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Visual State Space Model for Remote Sensing Binary Change Detection"],"prefix":"10.1049","volume":"19","author":[{"given":"Huagang","family":"Jin","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3723-7584","authenticated-orcid":false,"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China"}]}],"member":"265","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.12.005"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs16132355"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2024.3354310"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3348878"},{"key":"e_1_2_9_6_1","first-page":"4917","article-title":"A Weakly Supervised Bitemporal Scene Change Detection Approach for Pixel\u2010Level Building Damage Assessment Using Pre\u2010and Post\u2010Disaster High\u2010Resolution Remote Sensing Images","volume":"17","author":"Qiao W.","year":"2024","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"5","key":"e_1_2_9_7_1","first-page":"2858","article-title":"Slow Feature Analysis for Change Detection in Multispectral Imagery","volume":"52","author":"Chen W.","year":"2013","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2416635"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2013.03.006"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3093766"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3341437"},{"issue":"11","key":"e_1_2_9_12_1","first-page":"12084","article-title":"Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network","volume":"52","author":"Chen W.","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2956756"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2981051"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2022.08.012"},{"key":"e_1_2_9_16_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3158741","article-title":"A Deeply Supervised Attention Metric\u2010Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection","volume":"60","author":"Shi Q.","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3233187"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113371"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/10095020.2022.2085633"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"Z.Liu Y.Lin Y.Cao et\u00a0al. \u201cSwin Transformer: Hierarchical Vision Transformer Using Shifted Windows \u201d inProceedings of the IEEE\/CVF International Conference on Computer Vision(IEEE 2021) 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_2_9_21_1","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation With Transformers","volume":"34","author":"Xie E.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_22_1","unstructured":"A.Gu K.Goel andC.R\u00e9 \u201cEfficiently Modeling Long Sequences With Structured State Spaces\u201dpaper presented at the Tenth International Conference on Learning Representations (virtual) April2022."},{"key":"e_1_2_9_23_1","unstructured":"A.GuandT.Dao \u201cMamba: Linear\u2010Time Sequence Modeling With Selective State Spaces \u201d preprint arXiv December 1 2023 https:\/\/doi.org\/10.48550\/arXiv.2312.00752."},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2011.12.001"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2006.01.013"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apgeog.2011.10.010"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2650198"},{"key":"e_1_2_9_28_1","doi-asserted-by":"crossref","unstructured":"H.Chen C.Wu andB.Du \u201cDeep Siamese Multi\u2010Scale Convolutional Network for Change Detection in Multi\u2010Temporal VHR Images \u201d in10th International Workshop on the Analysis of Multitemporal Remote Sensing Images(IEEE 2019) 1\u20134.","DOI":"10.1109\/Multi-Temp.2019.8866947"},{"key":"e_1_2_9_29_1","doi-asserted-by":"crossref","unstructured":"R. C.Daudt B.Le Saux andA.Boulch \u201cFully Convolutional Siamese Networks for Change Detection \u201d in25th IEEE International Conference on Image Processing(IEEE 2018) 4063\u20134067.","DOI":"10.1109\/ICIP.2018.8451652"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.06.003"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2956756"},{"key":"e_1_2_9_32_1","first-page":"1","article-title":"SNUNet\u2010CD: A Densely Connected Siamese Network for Change Detection of VHR Images","volume":"19","author":"Fang K. L.","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3264802"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3336471"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112589"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113371"},{"key":"e_1_2_9_37_1","first-page":"1","article-title":"Change Detection On Remote Sensing Images Using Dual\u2010Branch Multilevel Intertemporal Network","volume":"61","author":"Feng Y.","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_38_1","first-page":"1","article-title":"Joint Spatio\u2010Temporal Modeling for Semantic Change Detection in Remote Sensing Images","volume":"62","author":"Ding L.","year":"2024","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_39_1","unstructured":"Dosovitskiy. Alexey \u201cAn Image is Worth 16\u00d7$\\times$16 Words: Transformers for Image Recognition at Scale \u201d preprint arXiv October 22 2020 https:\/\/arxiv.org\/abs\/2010.11929."},{"key":"e_1_2_9_40_1","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation With Transformers","volume":"34","author":"Xie E.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_41_1","first-page":"1","article-title":"Remote Sensing Image Change Detection With Transformers","volume":"60","author":"Hao C.","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_42_1","doi-asserted-by":"crossref","unstructured":"W. G. C.BandaraandV. M.Patel \u201cA Transformer\u2010Based Siamese Network for Change Detection \u201d inProceedings of International Geoscience and Remote Sensing Symposium(IEEE 2022) 207\u2013210.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"e_1_2_9_43_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3221492","article-title":"SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection","volume":"60","author":"Zhang C.","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_44_1","first-page":"1","article-title":"TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote\u2010Sensing Images","volume":"60","author":"Li Q.","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_45_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3327139","article-title":"VcT: Visual Change Transformer for Remote Sensing Image Change Detection","volume":"61","author":"Jiang B.","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_46_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3305334","article-title":"UCDFormer: Unsupervised Change Detection Using A Transformer\u2010Driven Image Translation","volume":"61","author":"Xu Q.","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_47_1","unstructured":"A.Gu K.Goel andC.R\u00c3l \u201cEfficiently Modeling Long Sequences With Structured State Spaces \u201d preprint arXiv November 1 2021 https:\/\/arxiv.org\/abs\/2111.00396."},{"key":"e_1_2_9_48_1","unstructured":"J. T.Smith A.Warrington andS. W.Linderman \u201cSimplified State Space Layers For Sequence Modeling \u201d preprint arXiv August 9 2022 https:\/\/doi.org\/10.48550\/arXiv.2208.04933."},{"key":"e_1_2_9_49_1","unstructured":"D. Y.Fu T.Dao K. K.Saab A. W.Thomas A.Rudra andC.R\u00c3 \u201cHungry Hungry Hippos: Towards Language Modeling With State Space Models \u201d preprint arXiv December 28 2022 https:\/\/doi.org\/10.48550\/arXiv.2212.14052."},{"key":"e_1_2_9_50_1","unstructured":"A.GuandT.Dao \u201cMamba: Linear\u2010Time Sequence Modeling With Selective State Spaces \u201d preprint arXiv December 4 2023 https:\/\/doi.org\/10.48550\/arXiv.2312.00752."},{"key":"e_1_2_9_51_1","unstructured":"Y.Liu Y.Tian Y.Zhao H.Yu Y.Wang andQ.Ye \u201cVmamba: Visual State Space Model \u201d inProceedings of the Thirty\u2010eighth Annual Conference on Neural Information Processing Systems(NeurIPS 2024)."},{"key":"e_1_2_9_52_1","unstructured":"L.Zhu B.Liao Q.Zhang X.Wang W.Liu andX.Wang \u201cVision Mamba: Efficient Visual Representation Learning With Bidirectional State Space Model \u201d inpaper presented at the International Conference on Machine Learning Vienna Austria July 21\u201327 2024."},{"key":"e_1_2_9_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102779"},{"key":"e_1_2_9_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2024.3407111"},{"key":"e_1_2_9_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2863224"},{"key":"e_1_2_9_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.10.015"},{"key":"e_1_2_9_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.11.004"},{"key":"e_1_2_9_58_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3158741","article-title":"A Deeply Supervised Attention Metric\u2010Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection","volume":"60","author":"Shi Q.","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_59_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12101662"},{"key":"e_1_2_9_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2858817"},{"key":"e_1_2_9_61_1","unstructured":"I.LoshchilovandF.Hutter \u201cDecoupled Weight Decay Regularization \u201d inpaper presented at the 7th International Conference on Learning Representations New Orleans LA May 6\u20139 2019."},{"key":"e_1_2_9_62_1","doi-asserted-by":"crossref","unstructured":"R. C.Daudt B.Le Saux andA.Boulch \u201cFully Convolutional Siamese Networks for Change Detection \u201d inProceedings of 25th IEEE International Conference on Image Processing(IEEE 2018) 4063\u20134067.","DOI":"10.1109\/ICIP.2018.8451652"},{"key":"e_1_2_9_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2956756"},{"key":"e_1_2_9_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.06.003"},{"key":"e_1_2_9_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3310208"},{"key":"e_1_2_9_66_1","first-page":"1","article-title":"SNUNet\u2010CD: A Densely Connected Siamese Network for Change Detection of VHR Images","volume":"19","author":"Fang S.","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_2_9_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3264802"},{"key":"e_1_2_9_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3510383"},{"key":"e_1_2_9_69_1","doi-asserted-by":"crossref","unstructured":"W. G. C.BandaraandV. M.Patel \u201cA Transformer\u2010Based Siamese Network for Change Detection \u201d inProceedings of International Geoscience and Remote Sensing Symposium(IEEE 2022) 207\u2013210.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"e_1_2_9_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3034752"},{"key":"e_1_2_9_71_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3221492","article-title":"SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection","volume":"60","author":"Zhang C.","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_72_1","first-page":"1","article-title":"TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote\u2010Sensing Images","volume":"60","author":"Li Q.","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_9_73_1","first-page":"1","article-title":"Relation Changes Matter: Cross\u2010Temporal Difference Transformer for Change Detection in Remote Sensing Images","volume":"61","author":"Zhang K.","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70214","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70214","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T23:25:38Z","timestamp":1776986738000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":72,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ipr2.70214"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70214","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-05-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70214"}}