ChessMamba: 结构感知的多时态空间交错用于遥感图像变化检测
ChessMamba: Structure-Aware Interleaving of State Spaces for Change Detection in Remote Sensing Images
AI总结：
ChessMamba通过结构感知的交错状态空间建模，提升多时态遥感图像变化检测的准确性和鲁棒性。
摘要：
多时态遥感影像中的变化检测（CD）在细粒度识别中面临重大挑战，这归因于异质性和时空不匹配。然而，现有基于视觉Transformer或状态空间模型的方法在时间序列化过程中通常会破坏局部结构一致性，导致在不匹配情况下判别线索被掩盖，从而阻碍可靠的变更定位。为了解决这个问题，我们引入了ChessMamba，一种结构感知框架，利用交错的状态空间建模来实现稳健的多时态输入变化检测。ChessMamba集成了一个轻量级的跨源交互模块，其两个关键创新是：（i）棋盘式交错，采用蛇形扫描顺序，将多时态特征序列化为单一前向传递中的统一序列，从而缩短交互路径，实现准确的变化定位；（ii）结构感知融合通过多扩张卷积，选择性地捕获每个单时态中的中心和角落邻域上下文。在三个变化检测任务上的全面评估，包括二分类CD、语义CD和多模态建筑损坏评估，表明ChessMamba有效融合了异质特征，并在现有最先进方法上实现了显著的准确性提升。相关代码将在：github.com/DingLei14/ChessMamba上提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
