Robust change detection for remote sensing images based on temporospatial interactive attention module

Abstract

Change Detection (CD) is a vital monitoring method in Earth observation, especially pertinent for land-use analysis, city management, and disaster damage assessment. However, in the era of constellation interconnection and air-sky collaboration, the changes in the Regions Of Interest (ROI) cause many false detections due to geometric perspective rotation and temporal style difference. In response to these challenges, we introduce CDNeXt, this framework elucidates a robust and efficient method for combining Siamese networks based on the pre-trained backbone with the innovative Temporospatial Interactive Attention Module (TIAM) for remote sensing imagery. The CDNeXt can be categorized into four primary components: Encoder, Interactor, Decoder, and Detector. Notably, the Interactor, powered by TIAM, queries and rebuilds spatial perspective dependencies and temporal style correlations from binary temporal features extracted by the Encoder to enlarge the difference of ROI change. Culminating the process, the Detector integrates the hierarchical features generated by the Decoder, subsequently producing a binary change mask. We have achieved new State-Of-The-Art (SOTA) performance in change detection, with our method surpassing existing techniques on four benchmark datasets: an F1 score of 82.63% on SYSU-CD, 87.14% on LEVIR-CD+, 66.71% on S2Looking, and 71.11% BANDON. To further validate the effectiveness of the TIAM, we compared it to other attention modules in both interactive and non-interactive modes. Our code is available on GitHub: https://github.com/wjj282439449/CDNeXt.