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

doi:10.3390/agriculture12081284

	HBRNet: Boundary Enhancement Segmentation Network for Cropland Extraction in High-Resolution Remote Sensing Images
	Sheng, Jiajia 3,4; Sun, Youqiang 4; Huang, He 2,4; Xu, Wenyu 3,4; Pei, Haotian 1,4; Zhang, Wei 1,4; Wu, Xiaowei 2
刊名	AGRICULTURE-BASEL
	2022-08-01
卷号	12
关键词	high-resolution remote sensing images semantic segmentation transformer boundary refinement cropland extraction
DOI	10.3390/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.
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关键词	WAVELET
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
内容类型	期刊论文
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).