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center","award":["602431001PQ"],"award-info":[{"award-number":["602431001PQ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing the quality of images shot over various geographical areas, particularly for high-resolution images. However, there are some shortcomings of the widely used Convolutional Neural Networks (CNNs) and Transformers-based CD methods. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Additionally, the commonly used information-fusion methods for pre- and post-change images often lead to information loss or redundancy, resulting in inaccurate edge detection. To address these issues, we propose an Iterative Mamba Diffusion Change Detection (IMDCD) approach to iteratively integrate various pieces of information and efficiently produce fine-grained CD maps. Specifically, the Swin-Mamba-Encoder (SME) within Mamba-CD (MCD) is employed as a semantic feature extractor, capable of modeling long-range relationships with linear computability. Moreover, we introduce the Variable State Space CD (VSS-CD) module, which extracts abundant CD features by training the matrix parameters within the designed State Space Change Detection (SS-CD). The computed high-dimensional CD feature is integrated into the noise predictor using a novel Global Hybrid Attention Transformer (GHAT) while low-dimensional CD features are utilized to calibrate prior CD results at each iterative step, progressively refining the generated outcomes. IMDCD exhibits a high performance across multiple datasets such as the CDD, WHU, LEVIR, and OSCD, marking a significant advancement in the methodologies within the CD field of RS. The code for this work is available on GitHub.<\/jats:p>","DOI":"10.3390\/rs16193651","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:45:27Z","timestamp":1727675127000},"page":"3651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Iterative Mamba Diffusion Change-Detection Model for Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"given":"Feixiang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China"},{"name":"School of Telecommunications Engineering, Xidian University, Xian 710071, China"}]},{"given":"Yihan","family":"Wen","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510700, China"}]},{"given":"Jiayi","family":"Sun","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510700, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7678-0005","authenticated-orcid":false,"given":"Peipei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518100, China"}]},{"given":"Liang","family":"Mao","sequence":"additional","affiliation":[{"name":"Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, China"}]},{"given":"Guanchong","family":"Niu","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510700, China"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"ref_1","first-page":"4205418","article-title":"Continuous Change Detection of Flood Extents with Multi-Source Heterogeneous Satellite Image Time Series","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. 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