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Study on Remote Sensing Classification Method of Long Baotan Wetland Based on CHRIS/PROBA

  • Received Date: 2010-11-20
  • This paper provides a new method of improving the classification accuracy of wetland in Long Baotan area in Qinghai Province,by studying the image transformation and band combinations of three angle images of +36°, 0° and -36°,which were devived from the multi-angle CHRIS hyperspectral remote sensing data. Firstly, the tasseled cap transformation was used to the 0° CHRIS image. Secondly, a new color composite image of RGB was generated by combining the humidity image of 0° with the 4-band (0.461μm) of +36° and -36° images, and then, the Support Vector Machine,SVM, a supervised classification method was carried out on the new RGB image.The studies showed that the classification accuracy of the new combination method in different angle images of CHRIS approached to 90.02%, which was greatly improved then 75.46% of traditional supervised classification accuracy, and it also provide an effective method to extract wetlands information.
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Study on Remote Sensing Classification Method of Long Baotan Wetland Based on CHRIS/PROBA

  • 1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Abstract: This paper provides a new method of improving the classification accuracy of wetland in Long Baotan area in Qinghai Province,by studying the image transformation and band combinations of three angle images of +36°, 0° and -36°,which were devived from the multi-angle CHRIS hyperspectral remote sensing data. Firstly, the tasseled cap transformation was used to the 0° CHRIS image. Secondly, a new color composite image of RGB was generated by combining the humidity image of 0° with the 4-band (0.461μm) of +36° and -36° images, and then, the Support Vector Machine,SVM, a supervised classification method was carried out on the new RGB image.The studies showed that the classification accuracy of the new combination method in different angle images of CHRIS approached to 90.02%, which was greatly improved then 75.46% of traditional supervised classification accuracy, and it also provide an effective method to extract wetlands information.

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