Multiregion Scale-Aware Network for Building Extraction From High-Resolution Remote Sensing Images

Building extraction is an essential task due to its relevance to urban planning and automatic surveying mapping activities. Despite the existing convolutional neural network-based methods that have achieved remarkable progress on building extraction from remote sensing images, the accurate extraction of buildings with extremely large variations of scales and layouts is still challenging. In this study, a novel multiregion scale-aware network is proposed to address these issues. The network consists of two key components. First, a multiregion attention module is proposed to capture long-range context dependencies and exploit different regions' attention information, alleviating the interference of cluttered backgrounds and variations in building layouts. With the multiscale features generated by a backbone network as input, a graph-based scale-aware structure is designed to model and reason the interactions between different scale features to enable a better understanding of multiscale features. Extensive experiments conducted on three datasets demonstrate that the proposed method achieves superior performance compared with the other state-of-the-art methods.

Automated summary

In this study, a novel multiregion scale-aware network is proposed to accurately extract buildings with varying scales and layouts in remote sensing images. The network utilizes a multiregion attention module to capture long-range context dependencies and a graph-based scale-aware structure to model and reason the interactions between different scale features. Extensive experiments demonstrate that the proposed method outperforms other state-of-the-art methods.