Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.
Identifieur interne : 000074 ( PubMed/Checkpoint ); précédent : 000073; suivant : 000075Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.
Auteurs : Zhen Zhou [République populaire de Chine] ; Jingfeng Huang [République populaire de Chine] ; Jing Wang [République populaire de Chine] ; Kangyu Zhang [République populaire de Chine] ; Zhaomin Kuang [République populaire de Chine] ; Shiquan Zhong [République populaire de Chine] ; Xiaodong Song [République populaire de Chine]Source :
- PloS one [ 1932-6203 ] ; 2015.
Descripteurs français
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- MESH :
- geographic : China.
- growth & development : Saccharum.
- Algorithms, Image Processing, Computer-Assisted, Remote Sensing Technology.
Abstract
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.
DOI: 10.1371/journal.pone.0142069
PubMed: 26528811
Affiliations:
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Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName><orgName type="university">Université de Zhejiang</orgName></affiliation></author><author><name sortKey="Wang, Jing" sort="Wang, Jing" uniqKey="Wang J" first="Jing" last="Wang">Jing Wang</name><affiliation wicri:level="4"><nlm:affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName><orgName type="university">Université de Zhejiang</orgName></affiliation></author><author><name sortKey="Zhang, Kangyu" sort="Zhang, Kangyu" uniqKey="Zhang K" first="Kangyu" last="Zhang">Kangyu Zhang</name><affiliation wicri:level="4"><nlm:affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName><orgName type="university">Université de Zhejiang</orgName></affiliation></author><author><name sortKey="Kuang, Zhaomin" sort="Kuang, Zhaomin" uniqKey="Kuang Z" first="Zhaomin" last="Kuang">Zhaomin Kuang</name><affiliation wicri:level="1"><nlm:affiliation>Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning</wicri:regionArea><wicri:noRegion>Nanning</wicri:noRegion></affiliation></author><author><name sortKey="Zhong, Shiquan" sort="Zhong, Shiquan" uniqKey="Zhong S" first="Shiquan" last="Zhong">Shiquan Zhong</name><affiliation wicri:level="1"><nlm:affiliation>Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning</wicri:regionArea><wicri:noRegion>Nanning</wicri:noRegion></affiliation></author><author><name sortKey="Song, Xiaodong" sort="Song, Xiaodong" uniqKey="Song X" first="Xiaodong" last="Song">Xiaodong Song</name><affiliation wicri:level="4"><nlm:affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName><orgName type="university">Université de Zhejiang</orgName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>China</term><term>Image Processing, Computer-Assisted</term><term>Remote Sensing Technology</term><term>Saccharum (growth & development)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="growth & development" xml:lang="en"><term>Saccharum</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Image Processing, Computer-Assisted</term><term>Remote Sensing Technology</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>République populaire de Chine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26528811</PMID><DateCreated><Year>2015</Year><Month>11</Month><Day>04</Day></DateCreated><DateCompleted><Year>2016</Year><Month>06</Month><Day>08</Day></DateCompleted><DateRevised><Year>2015</Year><Month>11</Month><Day>16</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>11</Issue><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.</ArticleTitle><Pagination><MedlinePgn>e0142069</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0142069</ELocationID><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Zhen</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Jingfeng</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Jing</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Kangyu</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuang</LastName><ForeName>Zhaomin</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhong</LastName><ForeName>Shiquan</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Song</LastName><ForeName>Xiaodong</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>11</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007091" MajorTopicYN="Y">Image Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058998" MajorTopicYN="Y">Remote Sensing Technology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D031786" MajorTopicYN="N">Saccharum</DescriptorName><QualifierName UI="Q000254" MajorTopicYN="Y">growth & development</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4631514</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>08</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>10</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>11</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>11</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>6</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">26528811</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0142069</ArticleId><ArticleId IdType="pii">PONE-D-15-36556</ArticleId><ArticleId IdType="pmc">PMC4631514</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Zhejiang</li></region><settlement><li>Hangzhou</li></settlement><orgName><li>Université de Zhejiang</li></orgName></list><tree><country name="République populaire de Chine"><region name="Zhejiang"><name sortKey="Zhou, Zhen" sort="Zhou, Zhen" uniqKey="Zhou Z" first="Zhen" last="Zhou">Zhen Zhou</name></region><name sortKey="Huang, Jingfeng" sort="Huang, Jingfeng" uniqKey="Huang J" first="Jingfeng" last="Huang">Jingfeng Huang</name><name sortKey="Kuang, Zhaomin" sort="Kuang, Zhaomin" uniqKey="Kuang Z" first="Zhaomin" last="Kuang">Zhaomin Kuang</name><name sortKey="Song, Xiaodong" sort="Song, Xiaodong" uniqKey="Song X" first="Xiaodong" last="Song">Xiaodong Song</name><name sortKey="Wang, Jing" sort="Wang, Jing" uniqKey="Wang J" first="Jing" last="Wang">Jing Wang</name><name sortKey="Zhang, Kangyu" sort="Zhang, Kangyu" uniqKey="Zhang K" first="Kangyu" last="Zhang">Kangyu Zhang</name><name sortKey="Zhong, Shiquan" sort="Zhong, Shiquan" uniqKey="Zhong S" first="Shiquan" last="Zhong">Shiquan Zhong</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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