Prototype Contrastive Learning for Building Extraction From Remote Sensing Images

Deep learning has contributed to the rapid development of building extraction tasks from remote sensing (RS) images. Existing models typically leverage a segmentation-head to predict results, where multichannel feature maps extracted by the network are directly output as single-channel predictions. However, it is rarely noticed that this process results in a loss of features, which can lead to incomplete extraction of smaller buildings. Besides, boundary-blurring is also a common problem in the task. Therefore, in this letter, we propose a Siamese prototype contrastive learning network (SPCL-Net) to address these two problems. In the network, a novel prototype contrastive learning (PCL) module is proposed to alleviate feature loss problem by applying contrastive learning between prototype vectors. In addition, a reverse boundary enhancement (RBE) module is proposed to facilitate the representation of building boundaries and mitigate the boundary-blurring problem. Experiments are conducted on two datasets, INRIA and WHU. Compared with existing models, the final results show that the proposed approach is better than theirs in terms of evaluation metrics intersection over union (IoU).