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中国科学院大学学报 ›› 2020, Vol. 37 ›› Issue (4): 483-489.DOI: 10.7523/j.issn.2095-6134.2020.04.007

• 环境科学与地理学 • 上一篇    下一篇

基于FD-RCF的高分辨率遥感影像耕地边缘检测

李森1,2, 彭玲1, 胡媛1,2, 池天河1   

  1. 1. 中国科学院遥感与数字地球研究所, 北京 100101;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2019-01-16 修回日期:2019-04-18 发布日期:2020-07-15
  • 通讯作者: 彭玲
  • 基金资助:
    国家自然科学基金面上项目(41471430)资助

FD-RCF-based boundary delineation of agricultural fields in high resolution remote sensing images

LI Sen1,2, PENG Ling1, HU Yuan1,2, CHI Tianhe1   

  1. 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-01-16 Revised:2019-04-18 Published:2020-07-15
  • Supported by:
     

摘要: 针对建立和更新田间图形数据库时,以高分辨率遥感影像为底图人工勾绘地块耗时费力这一问题,探索从边缘检测的角度实现对地块边缘的自动提取。在构建耕地地块边缘遥感影像数据集工作中,尝试深度学习边缘检测模型holistically-nested edge detection(HED)和richer convolutional features(RCF)的基础上,进一步改变模型特征融合方式,并采用空洞卷积结构,提出构建应用于遥感影像的边缘检测模型full dilated-RCF(FD-RCF),提取耕地地块边缘。实验表明,相关方法的精度评定F1值均能达到0.8以上。构建的FD-RCF模型表现最佳,其检测结果在ODS和OIS精度评定中F1值分别达到0.848 1和0.850 2,平均精度0.795 7。比较而言,FD-RCF方法检测结果画面更加清晰,能够显著提高田间地形数据的更新效率。

 

关键词: 深度学习, 高空间分辨率遥感影像, 边缘检测, 耕地, 地块

Abstract: High spatial resolution (high resolution) remote sensing images are reliable data sources for creating and updating field graphics databases. However, manual vectorization is a tedious process which costs much time and effort. In order to solve this problem, a method called full dilated-RCF (FD-RCF), which is based on dilated convolution in deep learning, is proposed. Compared with holistically-nested edge detection (HED) and richer convolutional features (RCF), FD-RCF mainly differentiates in the way of combining different results from different layers. FD-RCF consists of 6 stages and takes more care of the way of fusing these outputs from convolution layers into side-outputs. With dilated convolution, FD-RCF decreases the loss of information in deep layer. These methods can totally be used in detecting the boundary of agricultural fields. All of them get F1-values of over 0.8 in ODS and OIS. FD-RCF gets the highest F1-values of 0.848 1 and 0.850 2 in ODS and OIS, respectively, and the average precision of 0.795 7. The results gotten from FD-RCF are clearer than other methods and FD-RCF costs less time than manual vectorization.

Key words: deep learning, high resolution remote sensing images, boundary delineation, agricultural fields, land mass

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