BSSNet: Building Subclass Segmentation From Satellite Images Using Boundary Guidance and Contrastive Learning

Abstract

Building subclass segmentation, aimed at predicting classes of buildings (high-rise zone, low-rise zone, single high-rise, and single low-rise) from satellite images, is beneficial in numerous applications, including human geography, urban planning, and humanitarian aid. However, problems, such as complex scenes and similar characteristics of different building categories make it difficult for general models to balance the accuracy of localization and classification in building subclass segmentation. Therefore, this article proposes a novel network for building subclass segmentation called building subclass segmentation network (BSSNet), which uses two subnetworks to divide and conquer the problem. The first network guides the building locations through binary building segmentation, called localization network. The spatial gradient fusion module in the localization network improves the binary segmentation result by supervising the spatial gradient map of prediction. The second network is a classification network, which predicts building subclasses. Intermediate features of the second network are optimized by contrastive learning loss to improve feature consistency. Finally, predictions of the two networks are combined to obtain the final result. The experimental results demonstrate that our BSSNet can perform significant improvements on the Hainan dataset we produced and the xBD dataset. In particular, the BSSNet achieves the best performance compared to current methods on the Hainan dataset.

Keywords

• Building subclass segmentation
• contrastive learning loss
• convolutional neural network (CNN)
• feature fusion
• satellite image
• spatial gradient fusion (SGF)