  Change detection (CD) from remote sensing (RS) images using deep learning has
been widely investigated in the literature. It is typically regarded as a
pixel-wise labeling task that aims to classify each pixel as changed or
unchanged. Although per-pixel classification networks in encoder-decoder
structures have shown dominance, they still suffer from imprecise boundaries
and incomplete object delineation at various scenes. For high-resolution RS
images, partly or totally changed objects are more worthy of attention rather
than a single pixel. Therefore, we revisit the CD task from the mask prediction
and classification perspective and propose MaskCD to detect changed areas by
adaptively generating categorized masks from input image pairs. Specifically,
it utilizes a cross-level change representation perceiver (CLCRP) to learn
multiscale change-aware representations and capture spatiotemporal relations
from encoded features by exploiting deformable multihead self-attention
(DeformMHSA). Subsequently, a masked-attention-based detection transformers
(MA-DETR) decoder is developed to accurately locate and identify changed
objects based on masked attention and self-attention mechanisms. It
reconstructs the desired changed objects by decoding the pixel-wise
representations into learnable mask proposals and making final predictions from
these candidates. Experimental results on five benchmark datasets demonstrate
the proposed approach outperforms other state-of-the-art models. Codes and
pretrained models are available online (https://github.com/EricYu97/MaskCD).
