  Change detection is a widely adopted technique in remote sense imagery (RSI)
analysis in the discovery of long-term geomorphic evolution. To highlight the
areas of semantic changes, previous effort mostly pays attention to learning
representative feature descriptors of a single image, while the difference
information is either modeled with simple difference operations or implicitly
embedded via feature interactions. Nevertheless, such difference modeling can
be noisy since it suffers from non-semantic changes and lacks explicit guidance
from image content or context. In this paper, we revisit the importance of
feature difference for change detection in RSI, and propose a series of
operations to fully exploit the difference information: Alignment, Perturbation
and Decoupling (APD). Firstly, alignment leverages contextual similarity to
compensate for the non-semantic difference in feature space. Next, a difference
module trained with semantic-wise perturbation is adopted to learn more
generalized change estimators, which reversely bootstraps feature extraction
and prediction. Finally, a decoupled dual-decoder structure is designed to
predict semantic changes in both content-aware and content-agnostic manners.
Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD and
DSIFN-CD, demonstrating our proposed operations bring significant improvement
and achieve competitive results under similar comparative conditions. Code is
available at https://github.com/wangsp1999/CD-Research/tree/main/openAPD
