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arXiv · 2511.19882

ChessMamba: Structure-Aware Interleaving of State Spaces for Change Detection in Remote Sensing Images

Lei Ding·Tong Liu·Xuanguang Liu·Xiangyun Liu·Haitao Guo·Jun Lu
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Abstract

Change detection (CD) in multitemporal remote sensing imagery presents significant challenges for fine-grained recognition, owing to heterogeneity and spatiotemporal misalignment. However, existing methodologies based on vision transformers or state-space models typically disrupt local structural consistency during temporal serialization, obscuring discriminative cues under misalignment and hindering reliable change localization. To address this, we introduce ChessMamba, a structure-aware framework leveraging interleaved state-space modeling for robust CD with multi-temporal inputs. ChessMamba integrates a SpatialMamba encoder with a lightweight cross-source interaction module, featuring two key innovations: (i) Chessboard interleaving with snake scanning order, which serializes multi-temporal features into a unified sequence within a single forward pass, thereby shortening interaction paths and enabling direct comparison for accurate change localization; and (ii) Structure-aware fusion via multi-dilated convolutions, selectively capturing center-and-corner neighborhood contexts within each mono-temporal. Comprehensive evaluations on three CD tasks, including binary CD, semantic CD and multimodal building damage assessment, demonstrate that ChessMamba effectively fuses heterogeneous features and achieves substantial accuracy improvements over state-of-the-art methods.The relevant code will be available at: github.com/DingLei14/ChessMamba.

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Lei Ding, Tong Liu, Xuanguang Liu, Xiangyun Liu, Haitao Guo, Jun Lu. 2025-11-25. ChessMamba: Structure-Aware Interleaving of State Spaces for Change Detection in Remote Sensing Images. https://arxiv.org/abs/2511.19882

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