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of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise and pseudo-changes due to local receptive field limitations or computing resource constraints, which limits long-range dependency capture and feature integration, normally resulting in fragmented detections and high false positive rates. To address these challenges, we propose a tree topology Mamba-guided network (TTMGNet) based on Mamba architecture, which combines the Mamba architecture for effectively capturing global features, a unique tree topology structure for retaining fine local details, and a hierarchical feature fusion mechanism that enhances multi-scale feature integration and robustness against noise. Specifically, the a Tree Topology Mamba Feature Extractor (TTMFE) leverages the similarity of pixels to generate minimum spanning tree (MST) topology sequences, guiding information aggregation and transmission. This approach utilizes a Tree Topology State Space Model (TTSSM) to embed spatial and positional information while preserving the global feature extraction capability, thereby retaining local features. Subsequently, the Hierarchical Incremental Aggregation Module is utilized to gradually align and merge features from deep to shallow layers to facilitate hierarchical feature integration. Through residual connections and cross-channel attention (CCA), HIAM enhances the interaction between neighboring feature maps, ensuring that critical features are retained and effectively utilized during the fusion process, thereby enabling more accurate detection results in CD. The proposed TTMGNet achieved F1 scores of 92.31% on LEVIR-CD, 90.94% on WHU-CD, and 77.25% on CL-CD, outperforming current mainstream methods in suppressing the impact of background noise and pseudo-change and more accurately identifying change regions.<\/jats:p>","DOI":"10.3390\/rs16214068","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T09:57:36Z","timestamp":1730368656000},"page":"4068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["TTMGNet: Tree Topology Mamba-Guided Network Collaborative Hierarchical Incremental Aggregation for Change Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Hongzhu","family":"Wang","sequence":"first","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia"}]},{"given":"Zhaoyi","family":"Ye","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Chuan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2555-9199","authenticated-orcid":false,"given":"Liye","family":"Mei","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Cheng","family":"Lei","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-7613","authenticated-orcid":false,"given":"Du","family":"Wang","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Spatial-contextual information utilization framework for land cover change detection with hyperspectral remote sensed images","volume":"61","author":"Lv","year":"2023","journal-title":"IEEE Trans. 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