  Remote sensing change detection aims to compare two or more images recorded
for the same area but taken at different time stamps to quantitatively and
qualitatively assess changes in geographical entities and environmental
factors. Mainstream models usually built on pixel-by-pixel change detection
paradigms, which cannot tolerate the diversity of changes due to complex scenes
and variation in imaging conditions. To address this shortcoming, this paper
rethinks the change detection with the mask view, and further proposes the
corresponding: 1) meta-architecture CDMask and 2) instance network
CDMaskFormer. Components of CDMask include Siamese backbone, change extractor,
pixel decoder, transformer decoder and normalized detector, which ensures the
proper functioning of the mask detection paradigm. Since the change query can
be adaptively updated based on the bi-temporal feature content, the proposed
CDMask can adapt to different latent data distributions, thus accurately
identifying regions of interest changes in complex scenarios. Consequently, we
further propose the instance network CDMaskFormer customized for the change
detection task, which includes: (i) a Spatial-temporal convolutional
attention-based instantiated change extractor to capture spatio-temporal
context simultaneously with lightweight operations; and (ii) a scene-guided
axial attention-instantiated transformer decoder to extract more spatial
details. State-of-the-art performance of CDMaskFormer is achieved on five
benchmark datasets with a satisfactory efficiency-accuracy trade-off. Code is
available at https://github.com/xwmaxwma/rschange.
