CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model

Abstract

Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. However, the limitations of the limited receptive field of CNN and the high-computational complexity of Transformer remain. Recently, the Mamba architecture, based on state-space models, has demonstrated strong global receptive field capabilities and implements linear time complexity in computational processes. While some researchers have incorporated it into CD tasks, most have neglected the effective application of the Mamba selective scanning algorithm for modeling bitemporal image dependencies, resulting in suboptimal feature learning from bitemporal images. In this article, we propose a CD Mamba model (CD-STMamba), which can efficiently encode and decode bitemporal images interactively from multiple dimensions, thus enabling more accurate CD. Specifically, we propose a spatio-temporal interaction module (STIM), which can interact with bitemporal image features in multiple dimensions and fit with the Mamba architecture, allowing it to fully learn the global contextual information of the bitemporal input image. We also introduce a decoding block called the CD block, which can be fully decoded to learn multiple spatio-temporal relationships based on the characteristics of STIM. This block employs multiple change visual state space blocks internally to decode different spatio-temporal interactions and utilizes the change attention module to capture change features comprehensively for more accurate CD. The proposed CD-STMamba achieved state-of-the-art intersection over union (IoU) on three datasets, Wuhan University Building Change Detection Dataset (91.29% ), Sun Yat-Sen University Change Detection (73.45% ), and Change Detection Dataset (95.56% ).