Abstract: The paper employed a novel method based on NystrÖm algorithm to realize dimensionality reduction of hyperspectral remote sensing image. First, part of the samples was extracted randomly to form sub kernel matrix whose eigenvectors were computed. Then the process above was iterated to compute the new kernel and update the eigenvectors. Finally the image after dimensionality reduction was produced with the last eigenvectors. This method was prepared with KPCA in time consumption, the quantity of extraction feature information and classification effect with datasets OMIS and ROSIS employed. Experimental results show that with contrast to KPCA, SKPCA (Simplified KPCA) had comparative performance in feature extraction and classification effects but apparently higher computing speed.

Key words: Hyperspectral image, KPCA, NystrÖm algorithm, Dimensionality reduction, Classification