基于FD-RCF的高分辨率遥感影像耕地边缘检测

doi:10.7523/j.issn.2095-6134.2020.04.007

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

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. 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 Sen^1,2, PENG Ling¹, HU Yuan^1,2, CHI Tianhe¹

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/Abstract

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

中图分类号:

TP39

李森, 彭玲, 胡媛, 池天河. 基于FD-RCF的高分辨率遥感影像耕地边缘检测[J]. 中国科学院大学学报, 2020, 37(4): 483-489.

LI Sen, PENG Ling, HU Yuan, CHI Tianhe. FD-RCF-based boundary delineation of agricultural fields in high resolution remote sensing images[J]. , 2020, 37(4): 483-489.

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基于FD-RCF的高分辨率遥感影像耕地边缘检测

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

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