Edge Neighborhood Contrastive Learning for Building Change Detection

Abstract

Building change detection aims to identify the change in buildings in the same geographic area. Recently, many methods based on deep learning (DL) have achieved encouraging performance. However, some challenges remain in effectively exploiting the temporal–spatial correlation and achieving good discrimination in the neighborhood of the edge. To relieve these issues, we develop a selective attention module (SAM) to model the relationship between the semantic and the state (i.e., unchanged or changed) of the pixel, which is integrated into an existing metric learning-based architecture. Moreover, inspired by recent advances in contrastive learning, we present a novel edge neighborhood contrastive learning method to force the network to learn discriminative and compact features, leading to improving the accuracy of building change detection. Experimental results demonstrate that our method achieves competitive performance in terms of objective metrics and visual comparisons.