title: Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.
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.
authors.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.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhou
authors.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.
authors.forename: Jingfeng
authors.initials: J
authors.lastname: Huang
authors.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.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.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.
authors.forename: Kangyu
authors.initials: K
authors.lastname: Zhang
authors.affiliation: Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning, China.
authors.forename: Zhaomin
authors.initials: Z
authors.lastname: Kuang
authors.affiliation: Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster-Mitigation Research, Nanning, China.
authors.forename: Shiquan
authors.initials: S
authors.lastname: Zhong
authors.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.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Song
mesh_terms: D000465:Algorithms; D002681:China; D007091:Image Processing, Computer-Assisted; D058998:Remote Sensing Technology; D031786:Saccharum / Q000254:growth & development*