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

Zhou Z, Huang J, Wang J, Zhang K, Kuang Z, Zhong S, Song X - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Related in: MedlinePlus

Thematic map of sugarcane areas in Suixi County.I request permission for the open-access journal PLOS ONE to publish Fig 7 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
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pone.0142069.g007: Thematic map of sugarcane areas in Suixi County.I request permission for the open-access journal PLOS ONE to publish Fig 7 under the Creative Commons Attribution License (CCAL) CC BY 3.0.

Mentions: The prediction model built by the AdaBoost algorithm was applied to the entire study area; the classification results for the sugarcane growing area are shown in Fig 7. The total growing area of sugarcane was approximately 481.58 km2 in the 2013–2014 harvest year. According to the statistical data of the local agriculture department in 2014, the total acreage of sugarcane was 492.97 km2, and the relative classification accuracy was approximately 97.68%.


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

Zhou Z, Huang J, Wang J, Zhang K, Kuang Z, Zhong S, Song X - PLoS ONE (2015)

Thematic map of sugarcane areas in Suixi County.I request permission for the open-access journal PLOS ONE to publish Fig 7 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631514&req=5

pone.0142069.g007: Thematic map of sugarcane areas in Suixi County.I request permission for the open-access journal PLOS ONE to publish Fig 7 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
Mentions: The prediction model built by the AdaBoost algorithm was applied to the entire study area; the classification results for the sugarcane growing area are shown in Fig 7. The total growing area of sugarcane was approximately 481.58 km2 in the 2013–2014 harvest year. According to the statistical data of the local agriculture department in 2014, the total acreage of sugarcane was 492.97 km2, and the relative classification accuracy was approximately 97.68%.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Related in: MedlinePlus