DA2-Net: Integrating SAM2 with Domain Adaption and Difference Aggregation for Remote Sensing Change Detection

Abstract

Visual foundation models (VFMs) have been widely applied in the field of remote sensing (RS). However, they still face two main challenges when applied to precise RS change detection (RSCD) tasks in complex scenes. Firstly, the nonnegligible domain shift between natural scene and RS scene limits the direct application of VFMs to the RSCD task. Second, most of the existing RSCD methods may suffer from the boundary displacement problem due to the inadequate exploration of temporal differences for bi-temporal features. To address the above issues, this study proposes a Segment Anything Model 2 (SAM2)-based domain adaptive and spatial difference aggregation network (DA2-Net) for RSCD. The proposed DA2-Net has two main advantages. First, a hierarchical low-rank adaptation (LoRA) strategy is presented by introducing low-rank matrices at key positions of SAM2, which can inject inductive biases from the RS domain into the network and alleviate the domain shift problem. Second, a difference adaptive enhancement module (DAEM) is designed to explore temporal differences for hierarchical bi-temporal features. The DAEM provides respective attention weights for different information through a dual branch of global difference awareness and local detail optimization. Experimental results on SYSU-CD, WHU-CD, and LEVIR-CD datasets demonstrate the superiority of DA2-Net. Code is available at https://github.com/xuptheqi-hash/DA2Net.