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The method for detecting forest cover change in GF-1images by using KPCA |
Lingyu YIN1( ), Xianlin QIN1( ), Guifen SUN1, Shuchao LIU1, Xiaofeng ZU1, Xiaozhong CHEN2 |
1. Research Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China 2. ForestInformation Center of Sichuan Province, Chengdu 610081, China |
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Abstract In order to study the methods for forest cover change monitoring by using GF-1 images, the Yajiang County in Sichuan Province was selected as the research area to extract the information of forest coverage based on the two GF-1 WFV data. Firstly, the data were normalized by using the iteration re-weight multivariate alteration detection(IR-MAD)method. The two images were transformed by kernel principal component analysis(KPCA)method, and formed differencing image. Then, the changed area was extracted using the method of maximum between class variances(OTSU)for automatic threshold selection. Finally, the change detection results were validated using OTSU with the field sample data, and the extracted results were verified by way of precision test, and comparatively analyzed with the change vector analysis(CVA). The research results show that the overall accuracy of the two change detection methods is higher than 80%, and the overall accuracy of the KPCA method is 89.27%. The user precision of unchanged area is 93.88%, and the user's accuracy of changed area is 80.28%. The accuracy of the KPCA method is better than that of the algorithm based on the traditional CVA method. It is shown that, after the data transformation, KPCA algorithm can reduce the correlation between the variables and enhance the signal to noise ratio of the image, thus improving the recognition accuracy for the changed area.
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Keywords
GF-1
iteration re-weight multivariate alteration detection(IR-MAD)
kernel principal component analysis(KPCA)
method of maximum between class variance(OTSU)
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Issue Date: 08 February 2018
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