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

TL;DR

ChessMamba introduces a structure-aware, interleaved state-space framework for change detection in remote sensing images, improving accuracy by preserving local structural cues and enabling direct multi-temporal feature comparison.

Contribution

It proposes a novel Chessboard interleaving and structure-aware fusion approach that enhances change localization in multitemporal remote sensing imagery.

Findings

Achieves significant accuracy improvements over state-of-the-art methods.

Effectively fuses heterogeneous features for various change detection tasks.

Demonstrates robustness across binary, semantic, and multimodal change detection.

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.