DSFDcd: Joint Distribution Sampling and Feature Decoupling Deep Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD) aims to identify the regions of interest that have changed between dual-temporal images. However, most deep models predict CD results by extracting multiscale hybrid features, which could easily lead to ambiguous semantic boundaries; in addition, the existing feature acquisition tends to lack consideration of capturing their diversity, usually causing poor model generalization. Thus, this article decomposes the mixed features into change and invariant features jointly with stochastic distribution sampling and convolution, thus accomplishing robust RSCD based on decoupled representations. In the training stage, the posterior distribution of the uncoupled features is first learned through label calibration to train the prior distribution generator; then, robust feature decoupling is implemented combining the convolutional feature separator with reparameterized sampling over the decoupled posteriori distribution, and further aggregating the decoupled features through prototype learning; finally, the exceed-expectation (EE) loss regularizer is proposed to push or pull these positive and negative sample features to a more distant end, thereby increasing the interclass distance by boosting the predicted expectation. In the testing stage, the robust RSCD based on decoupled representation is accomplished through the feature separator, decoupled prior distribution random sampling, and the CD head without posterior distribution support. Experiments prove that DSFDcd has achieved remarkable results in terms of qualitative and quantitative metrics. Our codes will be available at https://github.com/iceking111/DSFDcd