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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: 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.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.

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

Multi-resolution segmentation using four different segmentation criteria.The base map of the multi-temporal HJ-1 CCD multi-spectral images with the following composition: R (4), G (3) and B (2). I request permission for the open-access journal PLOS ONE to publish Fig 3 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
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pone.0142069.g003: Multi-resolution segmentation using four different segmentation criteria.The base map of the multi-temporal HJ-1 CCD multi-spectral images with the following composition: R (4), G (3) and B (2). I request permission for the open-access journal PLOS ONE to publish Fig 3 under the Creative Commons Attribution License (CCAL) CC BY 3.0.

Mentions: In the process of segmenting the multi-temporal HJ-1 CCD time-series images, image objects were generated based on several adjustable criteria of homogeneity or heterogeneity in color and shape. The four parameters listed in Table 5 (i.e., scale, shape, color and compactness) need to be calibrated. We focused on adjusting the scale parameter because this parameter affects the average image object size (a larger value leads to larger objects and vice versa). To achieve better classification results, four different scale parameter values were used, and the results were compared using visual interpretation to determine the most suitable scale parameter value (Fig 3).


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)

Multi-resolution segmentation using four different segmentation criteria.The base map of the multi-temporal HJ-1 CCD multi-spectral images with the following composition: R (4), G (3) and B (2). I request permission for the open-access journal PLOS ONE to publish Fig 3 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.g003: Multi-resolution segmentation using four different segmentation criteria.The base map of the multi-temporal HJ-1 CCD multi-spectral images with the following composition: R (4), G (3) and B (2). I request permission for the open-access journal PLOS ONE to publish Fig 3 under the Creative Commons Attribution License (CCAL) CC BY 3.0.
Mentions: In the process of segmenting the multi-temporal HJ-1 CCD time-series images, image objects were generated based on several adjustable criteria of homogeneity or heterogeneity in color and shape. The four parameters listed in Table 5 (i.e., scale, shape, color and compactness) need to be calibrated. We focused on adjusting the scale parameter because this parameter affects the average image object size (a larger value leads to larger objects and vice versa). To achieve better classification results, four different scale parameter values were used, and the results were compared using visual interpretation to determine the most suitable scale parameter value (Fig 3).

Bottom Line: 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.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.

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