Scale-Robust Deep-Supervision Network for Mapping Building Footprints From High-Resolution Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

卷:14

Bo Du¹  Xin Su²  Shengkun Tang²  Haonan Guo³  Liangpei Zhang³ 

[1] National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China;

[2] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China;

[3] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;

关键词: Building footprint extraction;    convolutional neural network;    deep learning;    remote sensing image;   

DOI  :  10.1109/JSTARS.2021.3109237

来源: DOAJ

【 摘 要 】

Building footprint information is one of the key factors for sustainable urban planning and environmental monitoring. Mapping building footprints from remote sensing images is an important and challenging task in the earth observation field. Over the years, convolutional neural networks have shown outstanding improvements in the building extraction field due to their ability to automatically extract hierarchical features and make building predictions. However, as buildings are various in different sizes, scenes, and roofing materials, it is hard to precisely depict buildings of varied sizes, especially in large areas (e.g., nationwide). To tackle these limitations, we propose a novel deep-supervision convolutional neural network (denoted as DS-Net) for extracting building footprints from high-resolution remote sensing images. In the proposed network, we applied deep supervision with an extra lightweight encoder, which enables the network to learn representative building features of different scales. Furthermore, a scale attention module is designed to aggregate multiscale features and generate the final building prediction. Experiments on two publicly available building datasets, including the WHU Building Dataset and the Massachusetts Building Dataset, show the effectiveness of the proposed method. With only a 0.22-M increment of parameters compared with U-Net, the proposed DS-Net achieved an IoU of 90.4% on the WHU Building Dataset and 73.8% on the Massachusetts Dataset. DS-Net also outperforms the state-of-the-art building extraction methods on the two datasets, indicating the effectiveness of the proposed deep supervision and scale attention.

【 授权许可】

Unknown