期刊
SENSORS
卷 20, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/s20040993
关键词
semantic image segmentation; CRF; DSM; UAV; remote sensing
资金
- National Natural Science Foundation of China [61501228]
- Natural Science Foundation of Jiangsu [BK20140825]
- Aeronautical Science Foundation of China [20152052029, 20182052012]
- [NS2015040]
Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.
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