Affiliation:
1. Hunan University of Technology
Abstract
Kernel methods are famous for their efficiency and robustness in processing non-linear machine learning problems in the high dimensional feature space, and thus widely applied in image classification and detection. The proper principal components are selected for KPCA reconstruction according to noise features. Finally, the improved image is obtained by performing inverse method. Experimental results show that the proposed method can suppress noise interference in remote sensing images, and preserve the useful information of original data more completely.
Publisher
Trans Tech Publications, Ltd.
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