  Among the current mainstream change detection networks, transformer is
deficient in the ability to capture accurate low-level details, while
convolutional neural network (CNN) is wanting in the capacity to understand
global information and establish remote spatial relationships. Meanwhile, both
of the widely used early fusion and late fusion frameworks are not able to well
learn complete change features. Therefore, based on swin transformer V2 (Swin
V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to
inherit the advantages of both transformer and CNN and overcome the
shortcomings of existing networks in feature learning. Firstly, it captures the
change relationship features through the densely connected Swin V2 backbone,
and provides the low-level pre-changed and post-changed features through a CNN
branch. Based on these three change features, we accomplish accurate change
detection results. Secondly, combined with transformer and CNN, we propose
mixed feature pyramid (MFP) which provides inter-layer interaction information
and intra-layer multi-scale information for complete feature learning. MFP is a
plug and play module which is experimentally proven to be also effective in
other change detection networks. Further more, we impose a self-supervision
strategy to guide a new CNN branch, which solves the untrainable problem of the
CNN branch and provides the semantic change information for the features of
encoder. The state-of-the-art (SOTA) change detection scores and fine-grained
change maps were obtained compared with other advanced methods on four commonly
used public remote sensing datasets. The code is available at
https://github.com/DalongZ/SwinV2DNet.
