期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 9, 期 2, 页码 312-316出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2011.2167212
关键词
Feature extraction; k-means; kernel method; Parzen windowing; Renyi entropy; spectral clustering
类别
资金
- Spanish Ministry for Science and Innovation [AYA2008-05965-C04-03, CSD2007-00018]
- [UV-INV-AE11-41223]
This letter proposes the kernel entropy component analysis for clustering remote sensing data. The method generates nonlinear features that reveal structure related to the Renyi entropy of the input space data set. Unlike other kernel feature-extraction methods, the top eigenvalues and eigenvectors of the kernel matrix are not necessarily chosen. Data are interestingly mapped with a distinct angular structure, which is exploited to derive a new angle-based spectral clustering algorithm based on the mapped data. An out-of-sample extension of the method is also presented to deal with test data. We focus on cloud screening from Medium Resolution Imaging Spectrometer images. Several images are considered to account for the high variability of the problem. Good results obtained show the suitability of the proposal.
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