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HBRNet: Boundary Enhancement Segmentation Network for Cropland Extraction in High-Resolution Remote Sensing Images

文献类型：期刊论文

作者Sheng, Jiajia3,4; Sun, Youqiang4; Huang, He2,4; Xu, Wenyu3,4; Pei, Haotian1,4; Zhang, Wei1,4; Wu, Xiaowei2
刊名AGRICULTURE-BASEL
出版日期2022-08-01
卷号12
关键词high-resolution remote sensing images semantic segmentation transformer boundary refinement cropland extraction
DOI10.3390/agriculture12081284
通讯作者Huang, He(hhuang@iim.ac.cn)
英文摘要Cropland extraction has great significance in crop area statistics, intelligent farm machinery operations, agricultural yield estimates, and so on. Semantic segmentation is widely applied to remote sensing image cropland extraction. Traditional semantic segmentation methods using convolutional networks result in a lack of contextual and boundary information when extracting large areas of cropland. In this paper, we propose a boundary enhancement segmentation network for cropland extraction in high-resolution remote sensing images (HBRNet). HBRNet uses Swin Transformer with the pyramidal hierarchy as the backbone to enhance the boundary details while obtaining context. We separate the boundary features and body features from the low-level features, and then perform a boundary detail enhancement module (BDE) on the high-level features. Endeavoring to fuse the boundary features and body features, the module for interaction between boundary information and body information (IBBM) is proposed. We select remote sensing images containing large-scale cropland in Yizheng City, Jiangsu Province as the Agricultural dataset for cropland extraction. Our algorithm is applied to the Agriculture dataset to extract cropland with mIoU of 79.61%, OA of 89.4%, and IoU of 84.59% for cropland. In addition, we conduct experiments on the DeepGlobe, which focuses on the rural areas and has a diversity of cropland cover types. The experimental results indicate that HBRNet improves the segmentation performance of the cropland.
WOS关键词WAVELET
资助项目National Key Research and Development Program of China[2021YFD200060102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28120400]
WOS研究方向Agriculture
语种英语
出版者MDPI
WOS记录号WOS:000846386300001
资助机构National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/131918]  
专题中国科学院合肥物质科学研究院
通讯作者Huang, He
作者单位1.Anhui Univ, Inst Phys Sci, Hefei 230601, Peoples R China
2.Anhui Zhongke Intelligent Sence Ind Technol Res I, Wuhu 241070, Peoples R China
3.USTC, Sci Isl Branch, Grad Sch, Hefei 230026, Peoples R China
4.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Sheng, Jiajia,Sun, Youqiang,Huang, He,et al. HBRNet: Boundary Enhancement Segmentation Network for Cropland Extraction in High-Resolution Remote Sensing Images[J]. AGRICULTURE-BASEL,2022,12.
APA Sheng, Jiajia.,Sun, Youqiang.,Huang, He.,Xu, Wenyu.,Pei, Haotian.,...&Wu, Xiaowei.(2022).HBRNet: Boundary Enhancement Segmentation Network for Cropland Extraction in High-Resolution Remote Sensing Images.AGRICULTURE-BASEL,12.
MLA Sheng, Jiajia,et al."HBRNet: Boundary Enhancement Segmentation Network for Cropland Extraction in High-Resolution Remote Sensing Images".AGRICULTURE-BASEL 12(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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