GLMamba: A Global–Local Mamba Network for Efficient Remote Sensing Change Detection

With the rapid advancement of deep learning, convolutional neural networks (CNNs) and Transformers have become two mainstream architectures for remote sensing change detection (CD). CNNs are effective at extracting local features but struggle to model long-range dependencies due to limited receptive fields. Transformers capture global contextual information through self-attention, yet their high computational cost limits their practicality for high-resolution remote sensing imagery. To balance global modeling capability and local detail representation, this article introduces the Mamba architecture into CD and proposes global–local Mamba (GLMamba), an efficient network capable of jointly modeling long-range dependencies and fine-grained spatial details. Built upon the state-space model, GLMamba establishes a unified global–local modeling framework. In the encoding stage, the network extracts global features via the state space structure and enhances multiscale semantic aggregation through the Change-aware Semantic Aggregation Module. The Bitemporal Fusion Attention (BFA) Module further captures long-range cross-temporal dependencies and strengthens spatial detail representation. In the decoding stage, the Cross-Space Interaction Enhancement Module employs cross-attention to improve feature discrimination and spatial consistency in change regions. Experiments on LEVIR-CD, GZ-CD, and SYSU-CD demonstrate that GLMamba consistently outperforms state-of-the-art methods in CD accuracy, boundary preservation, and robustness to complex scenarios, while maintaining low parameter count and computational cost. These results validate the effectiveness and superiority of the proposed method.