Building Change Detection in High-Resolution Remote-Sensing Images Based on Deep Learning

To overcome low detection accuracy, false and leak detections for medium- and small-scale targets, rough segmentation for building boundary of traditional semantic segmentation network, we propose a high-resolution remote-sensing image building change detection method based on deep learning. The proposed method adopts the coding-decoding structure. At the coding stage, the residual network is used to extract the image features. The dilated convolution and pyramid pooling module are introduced in the deepest features of the encoder to enlarge the receptive field and extract the multiscale image features. At the decoding stage, the attention module highlights the useful features, and the features with different scales and resolutions are aggregated. We performed experiments on large-scale remote-sensing building change detection datasets. The results show that the proposed method can obtain deep-layer semantic information and pay attention to detailed information. It also has a considerable improvement in precision, recall, and F1 score. Additionally, the proposed method performs better than other semantic segmentation networks in multiscale target detection and building boundary extraction.