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

Jiajia Sheng, Youqiang Sun, He Huang (), Wenyu Xu, Haotian Pei, Wei Zhang and Xiaowei Wu
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Jiajia Sheng: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Youqiang Sun: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
He Huang: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Wenyu Xu: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Haotian Pei: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Wei Zhang: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Xiaowei Wu: Anhui Zhongke Intelligent Sence Industrial Technology Research Institute, Wuhu 241070, China

Agriculture, 2022, vol. 12, issue 8, 1-22

Abstract: 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.

Keywords: high-resolution remote sensing images; semantic segmentation; transformer; boundary refinement; cropland extraction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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