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Change detection is a widely adopted technique in remote sense imagery (RSI)analysis in the discovery of long-term geomorphic evolution. To highlight theareas of semantic changes, previous effort mostly pays attention to learningrepresentative feature descriptors of a single image, while the differenceinformation is either modeled with simple difference operations or implicitlyembedded via feature interactions. Nevertheless, such difference modeling canbe noisy since it suffers from non-semantic changes and lacks explicit guidancefrom image content or context. In this paper, we revisit the importance offeature difference for change detection in RSI, and propose a series ofoperations to fully exploit the difference information: Alignment, Perturbationand Decoupling (APD). Firstly, alignment leverages contextual similarity tocompensate for the non-semantic difference in feature space. Next, a differencemodule trained with semantic-wise perturbation is adopted to learn moregeneralized change estimators, which reversely bootstraps feature extractionand prediction. Finally, a decoupled dual-decoder structure is designed topredict semantic changes in both content-aware and content-agnostic manners.Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD andDSIFN-CD, demonstrating our proposed operations bring significant improvementand achieve competitive results under similar comparative conditions. Code isavailable at https://github.com/wangsp1999/CD-Research/tree/main/openAPD

Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection