Limits...
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

Study area in Suixi County, China.HJ-1 CCD image acquired on 26 October 2013 with a composition of R (4), G (3) and B (2). The HJ-1 CCD images were downloaded from the China Center for Resources Satellite Data and Application. I request permission for the open-access journal PLOS ONE to publish Fig 1 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4631514&req=5

pone.0142069.g001: Study area in Suixi County, China.HJ-1 CCD image acquired on 26 October 2013 with a composition of R (4), G (3) and B (2). The HJ-1 CCD images were downloaded from the China Center for Resources Satellite Data and Application. I request permission for the open-access journal PLOS ONE to publish Fig 1 under the Creative Commons Attribution License (CCAL) CC BY 3.0.

Mentions: The study area, Suixi County, is located north of the Leizhou Peninsula in Guangdong Province (Fig 1). The terrain in this area is relatively flat, and the mean elevation is approximately 40 m above sea level. Suixi County has a subtropical maritime monsoon climate with a mean annual temperature of approximately 22.8°C and an annual precipitation between 1700–1800 mm. Suixi County is a major sugarcane planting area in Guangdong Province, with approximately 467 km2 of planted area year round. In addition to sugarcane, the major vegetation in this region includes rice, peanut, banana, grass, pineapple, pitaya, mango and eucalyptus. However, the dominant types of crops in terms of area are rice and peanut.


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)

Study area in Suixi County, China.HJ-1 CCD image acquired on 26 October 2013 with a composition of R (4), G (3) and B (2). The HJ-1 CCD images were downloaded from the China Center for Resources Satellite Data and Application. I request permission for the open-access journal PLOS ONE to publish Fig 1 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.g001: Study area in Suixi County, China.HJ-1 CCD image acquired on 26 October 2013 with a composition of R (4), G (3) and B (2). The HJ-1 CCD images were downloaded from the China Center for Resources Satellite Data and Application. I request permission for the open-access journal PLOS ONE to publish Fig 1 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
Mentions: The study area, Suixi County, is located north of the Leizhou Peninsula in Guangdong Province (Fig 1). The terrain in this area is relatively flat, and the mean elevation is approximately 40 m above sea level. Suixi County has a subtropical maritime monsoon climate with a mean annual temperature of approximately 22.8°C and an annual precipitation between 1700–1800 mm. Suixi County is a major sugarcane planting area in Guangdong Province, with approximately 467 km2 of planted area year round. In addition to sugarcane, the major vegetation in this region includes rice, peanut, banana, grass, pineapple, pitaya, mango and eucalyptus. However, the dominant types of crops in terms of area are rice and peanut.

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