HJ-1A satellite remote sensing data classification based on KPCA and FCM
BAI Yang1,2, ZHAO Yindi1,2
1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
2. Key Laboratory for Land Environment and Disaster Monitoring of SBSM, Xuzhou 221116, China
In order to improve the remote sensing data classification accuracy of the environment and disaster monitoring and forecasting small satellite constellation 1A (HJ-1A) Star, the authors first fused hyperspectral imager data and CCD multispectral imagery by the Gram-Schmidt fusion algorithm, and then applied dimensionality reduction to the fused hyperspectral image by using principal component analysis (PCA) and kernel principal component analysis (KPCA). Gaussian, linear and polynomial kernel functions were employed during KPCA dimensionality reduction, and the polynomial kernel function was selected with its highest accumulative contribution rate according to the evaluation results of feature extraction. Finally, the fused hyperspectral image, the PCA image and the KPCA image with the polynomial kernel function were classified using the fuzzy C-means algorithm (FCM), respectively. The experimental results show that, for the fused hyperspectral image, the feature extraction based on KPCA can increase computational efficiency and improve the classification accuracy.
白杨, 赵银娣. 基于KPCA和FCM的HJ-1A星遥感数据分类[J]. 国土资源遥感, 2013, 25(1): 71-76.
BAI Yang, ZHAO Yindi. HJ-1A satellite remote sensing data classification based on KPCA and FCM. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 71-76.
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2013, Vol. 25 
