HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

Remote Sensing

Zhiyong Xu¹  Tianxiang Zhang¹  Weicun Zhang¹  Jiangyun Li¹ 

[1] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;

关键词: semantic segmentation;    remote sensing;    deep learning;    high resolution;    global context information;    boundary;   

DOI  :  10.3390/rs13010071

来源: DOAJ

【 摘 要 】

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

【 授权许可】

Unknown