Building Instance Segmentation and Boundary Regularization from High-Resolution Remote Sensing Images

Building extraction from remote sensing images using convolutional neural networks (CNNs) has been an active research topic in recent years. Most results obtained by CNN-based algorithms, however, still have common issues with the precision of the delineation of building outlines and the separation of different buildings. Recently, efforts have been made towards the automation of building outline regularization. This paper employs a new instance segmentation framework named Hybrid Task Cascade (HTC) as baseline model, integrating detection and segmentation as a joint multi-stage processing. We further integrate regularization methods such as convex hull and Douglas-Peucker algorithm to obtain accurately segmented edges. The method is tested on the crowdAI benchmark dataset by comparing with alternative state-of-the-art models (i.e., Mask R-CNN). The results show that our method achieves better instance segmentation results and improves the results in terms of geometric regularity of building segments.