中文版 | English
Title

Automatic grassland degradation estimation using deep learning

Author
Corresponding AuthorZheng,Feng
DOI
Publication Years
2019
ISSN
1045-0823
Source Title
Volume
2019-August
Pages
6028-6034
Abstract
Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.
SUSTech Authorship
Corresponding
Language
English
URL[Source Record]
Scopus EID
2-s2.0-85074939389
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/401734
DepartmentSouthern University of Science and Technology
Affiliation
1.Dept. of Computer Science and Technology,Tsinghua University,China
2.PCL Research Center of Networks and Communications,Peng Cheng Laboratory,China
3.Baidu,Inc.,
4.Dept. of Computer Technology and Applications,Qinghai University,China
5.Dept. of Computer Science and Engineering,Southern University of Science and Technology,China
Corresponding Author AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Yan,Xiyu,Jiang,Yong,Chen,Shuai,et al. Automatic grassland degradation estimation using deep learning[C],2019:6028-6034.
Files in This Item:
There are no files associated with this item.
Related Services
Fulltext link
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Yan,Xiyu]'s Articles
[Jiang,Yong]'s Articles
[Chen,Shuai]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Yan,Xiyu]'s Articles
[Jiang,Yong]'s Articles
[Chen,Shuai]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yan,Xiyu]'s Articles
[Jiang,Yong]'s Articles
[Chen,Shuai]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.