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

doi:10.7523/j.issn.2095-6134.2020.04.007

Abstract

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

CLC Number:

TP39

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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