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Multi-scale patch based box kernels for hyperspectral image classification
Peng J.2; Zhou Y.1; Chen C.L.P.1
2014
Conference NameIEEE International Conference on Systems, Man, and Cybernetics (SMC)
Source PublicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2014-January
IssueJanuary
Pages3203-3208
Conference DateOCT 05-08, 2014
Conference PlaceSan Diego, CA
Abstract

Integrating labeled pixels with prior knowledge of hyperspectral spatial homogeneous regions, we propose a region-based hyperspectral image classification method, called the support vector machine with the multi-scale patch based box kernel (SVM-MPBK). It models the local homogeneous region of each pixel as a box, and measures the similarity between different box regions using box kernel. The box is represented as multidimensional intervals computed band by band in a neighborhood pixel patch. Using multi-scale patches to calculate box, SVM-MPBK fuses the complementary classification results in different scales by a majority voting. Experimental results on benchmark hyperspectral data sets demonstrate the effectiveness of SVM-MPBK.

KeywordBox Kernels Classification Hyperspectral Image Multi-scale Support Vector Machine
DOIhttp://doi.org/10.1109/smc.2014.6974421
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000370963703055
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Citation statistics
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Universidade de Macau
2.Hubei University
Recommended Citation
GB/T 7714
Peng J.,Zhou Y.,Chen C.L.P.. Multi-scale patch based box kernels for hyperspectral image classification[C],2014:3203-3208.
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