Cargando…

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we dev...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Zhen, Huang, Jingfeng, Wang, Jing, Zhang, Kangyu, Kuang, Zhaomin, Zhong, Shiquan, Song, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631514/
https://www.ncbi.nlm.nih.gov/pubmed/26528811
http://dx.doi.org/10.1371/journal.pone.0142069
_version_ 1782398879087460352
author Zhou, Zhen
Huang, Jingfeng
Wang, Jing
Zhang, Kangyu
Kuang, Zhaomin
Zhong, Shiquan
Song, Xiaodong
author_facet Zhou, Zhen
Huang, Jingfeng
Wang, Jing
Zhang, Kangyu
Kuang, Zhaomin
Zhong, Shiquan
Song, Xiaodong
author_sort Zhou, Zhen
collection PubMed
description Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.
format Online
Article
Text
id pubmed-4631514
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-46315142015-11-13 Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost Zhou, Zhen Huang, Jingfeng Wang, Jing Zhang, Kangyu Kuang, Zhaomin Zhong, Shiquan Song, Xiaodong PLoS One Research Article Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. Public Library of Science 2015-11-03 /pmc/articles/PMC4631514/ /pubmed/26528811 http://dx.doi.org/10.1371/journal.pone.0142069 Text en © 2015 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhou, Zhen
Huang, Jingfeng
Wang, Jing
Zhang, Kangyu
Kuang, Zhaomin
Zhong, Shiquan
Song, Xiaodong
Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
title Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
title_full Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
title_fullStr Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
title_full_unstemmed Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
title_short Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
title_sort object-oriented classification of sugarcane using time-series middle-resolution remote sensing data based on adaboost
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631514/
https://www.ncbi.nlm.nih.gov/pubmed/26528811
http://dx.doi.org/10.1371/journal.pone.0142069
work_keys_str_mv AT zhouzhen objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost
AT huangjingfeng objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost
AT wangjing objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost
AT zhangkangyu objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost
AT kuangzhaomin objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost
AT zhongshiquan objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost
AT songxiaodong objectorientedclassificationofsugarcaneusingtimeseriesmiddleresolutionremotesensingdatabasedonadaboost