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Classification of hyperspectral image based on morphological profiles and multi-kernel SVM

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2 Author(s)
Kun Tan ; Key Lab. for Land Environ. & Disaster Monitoring of State Bur. of Surveying & Mapping (SBSM) of China, China Univ. of Min. & Technol., Xuzhou, China ; Peijun Du

A method is proposed for the classification of hyperspectral data with high spatial resolution by Support Vector Machine (SVM) with multiple kernels. The approach is an extension of previous sole-kernel classifiers by integrating spectral features with spatial or structural features for hyperspectral classification. Using Support Vector Machine (SVM) as the classifier, different multi-kernel SVM classifiers were constructed and tested using the Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands to evaluate the performance and accuracy of the proposed multi-kernel classifier. The results show that integrating the spectral and morphological profile (MP) features, the multi-kernel SVM classifiers obtain more accurate classification results than sole-kernel SVM classifier. Moreover, when the multi-kernel SVM classifier is used, the combination the first seven principal components derived from Principal Components Analysis (PCA) and MP provided the highest accuracy (91.05%).

Published in:

Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on

Date of Conference:

14-16 June 2010