DSQNet: Enhancing Change Detection Network via Deep Semantics Query for Remote Sensing Images

Change detection from remote sensing (RS) images has made significant progress in many applications including environmental protection and agricultural monitoring. Recently, RS change detection algorithms mainly focus on feature interaction between the bitemporal images. However, there is a challenge in existing methods regarding how to focus more attention on local prominent features and specifically enhance their salience during the interaction of representations. For this issue, this letter presents a change detection network DSQNet that marks multiple regions of interest using query vectors with deep semantics and explicitly searches the heterogeneous features for focused interaction. Specifically, DSQNet uses bidirectional matching query (BMQ) module to effectively perceive local relevant features by feature space query for cross matching and adequately enhance the context relationship within specific regions during the interaction, which helps the model better learn the idea of potential change. Moreover, to make the query vectors and the feature representation aligned in the semantic space, we further propose the deep semantic adjustment feature pyramid (DSP) module. It realizes interlayer feature adjustment from the inside out in the pyramid and enables query vectors to represent extremely rich semantics, improving query efficiency. Experimental results on four benchmark datasets show that DSQNet achieves better performance than other advanced change detection networks. The codes are available at https://github.com/QinZelin/DSQNet/.