DocumentCode :
3690295
Title :
Hyperspectral image classification using Gaussian process models
Author :
Michael Ying Yang;Wentong Liao;Bodo Rosenhahn;Zheng Zhang
Author_Institution :
Computer Vision Lab (CVLD) TU Dresden, Germany
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1717
Lastpage :
1720
Abstract :
Hyperspectral image processing has been a very dynamic area in remote sensing and other applications since last decades. Hyperspectral images provide abundant spectral information to identify and distinguish spectrally similar materials. Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for classifying hyper-spectral images. Many sophisticated kernel functions have been provided for kernel-based methods. However, different kernel functions has different performance in different applications. This paper introduces GP models with different kernel functions for classifying hyperspectral images. We first provided the mathematical formulation of GP models for classification. Then, several popular kernel functions and their hyperparaeters selection for GP models are introduced. The experiment are performed on three benchmark datasets to evaluate the performances of different kernel functions in terms of classification accuracy. Their performances are compared with each other and discussed in detailed.
Keywords :
"Kernel","Hyperspectral imaging","Accuracy","Training","Gaussian processes"
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International
ISSN :
2153-6996
Electronic_ISBN :
2153-7003
Type :
conf
DOI :
10.1109/IGARSS.2015.7326119
Filename :
7326119
Link To Document :
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