VISION-LANGUAGE JOINT LEARNING FOR BOX-SUPERVISED CHANGE DETECTION IN REMOTE SENSING

Change detection (CD) in remote sensing aims at revealing land cover changes according to the category of the ground objects. However, the category information is always missing in current popular vision-based CD methods. Considering that language analysis is really good at identifying different categories, a vision-language joint learning method is proposed in this paper, which consists of two vision-language joint representation (VLJR) modules and a changed instance segmentation (CIS) module. The former combines image features and language features with the help of text encoder and Transformer. The latter generates the final pixel-level CD result with only box-level labeled samples by level-set evolution and box matching supervision, which reduces manual-labor to a large extent. Tested on representative WHU datasets, the proposed method achieves comparable results to fully-supervised CD methods and is ahead of the other weakly-supervised methods.