International Journal of Computational Intelligence Systems

Volume 13, Issue 1, 2020, Pages 1393 - 1403

Woodland Labeling in Chenzhou, China, via Deep Learning Approach

Authors
Wei Wang1, ORCID, Yujing Yang1, Ji Li1, Yongle Hu2, Yanhong Luo3, *, Xin Wang1, *
1 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2 Hunan Children's Hospital, Changsha 410000, China
3 School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China
*Corresponding authors. Email: mfxgz123@163.com; wangxin@csust.edu.cn
Corresponding Author
Yanhong Luo
Received 7 June 2020, Accepted 7 September 2020, Available Online 16 September 2020.
DOI
https://doi.org/10.2991/ijcis.d.200910.001How to use a DOI?
Keywords
Woodland labeling, Convolutional network, Deep learning, Dense fully convolutional network (DFCN)
Abstract

In order to complete the task of the woodland census in Chenzhou, China, this paper carries out a remote sensing survey on the terrain of this area to produce a data set, and used deep learning methods to label the woodland. There are two main improvements in our paper: Firstly, this paper comparatively analyzes the semantic segmentation effects of different deep learning models on remote sensing image datasets in Chenzhou. Secondly, this paper proposed a dense fully convolutional network (DFCN) which combines dense network with FCN model and achieves good semantic segmentation effect. DFCN method is used to label the woodland in Gaofen-2 (GF-2) remote sensing images in Chenzhou. Under the same experimental conditions, the labeling results are compared with the original FCN, SegNet, dilated convolutional network, and so on. In these experiments, the global pixel accuracy of DFCN is 91.5%, and the prediction accuracy of the “woodland” class is 93%, both of them perform better than that of the other methods. In other indicators, our method also has better performance. Using the method of this paper, we have completed the land feature labeling of Chenzhou area and provided it to customers.

Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
1393 - 1403
Publication Date
2020/09
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.200910.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Wei Wang
AU  - Yujing Yang
AU  - Ji Li
AU  - Yongle Hu
AU  - Yanhong Luo
AU  - Xin Wang
PY  - 2020
DA  - 2020/09
TI  - Woodland Labeling in Chenzhou, China, via Deep Learning Approach
JO  - International Journal of Computational Intelligence Systems
SP  - 1393
EP  - 1403
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200910.001
DO  - https://doi.org/10.2991/ijcis.d.200910.001
ID  - Wang2020
ER  -