SVM Classification of Hyperspectral Image Based on Wavelet Kernel Minimum Noise Fraction

BAI Lin; LIU Panzhi; HUI Meng

doi:10.13203/j.whugis20140209

Volume 41 Issue 5

May 2016

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Geomatics and Information Science of Wuhan University > 2016 > 41(5): 624-628,644. > DOI: 10.13203/j.whugis20140209

BAI Lin, LIU Panzhi, HUI Meng. SVM Classification of Hyperspectral Image Based on Wavelet Kernel Minimum Noise Fraction[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 624-628,644. DOI: 10.13203/j.whugis20140209

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SVM Classification of Hyperspectral Image Based on Wavelet Kernel Minimum Noise Fraction

School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China

Funds: The National Natural Science Foundation of China, No. 51407012; the Special Fund for Basic Scientific Research of Central Colleges, Chang'an University, Nos. 310832163402, 310832161001.

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Received Date: September 21, 2014
Published Date: May 04, 2016

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Abstract

Linear features extraction methods for hyperspectral imaging reduce feature class separability. Aiming to solve these problems, this paper introduces a novel kernel method based on minimum noise fraction transformation. This method focuses on the kernel function in the minimum noise fraction transformation and replacing the traditional kernel function with a wavelet kernel function. This new method improves the nonlinear mapping capability of kernel minimum noise fraction transformation by exploiting the features of multi-resolution analysis. Classification experiments on hyperspectral image data combined the novel kernel minimum noise fraction transformation and support vector machine; simulation results show that the wavelet kernel minimum noise fraction transformation method is suitable for the nonlinear characteristics of hyperspectral images. The proposed method was applied to HYDICE and AVIRIS data, and compared with other algorithms. Classification accuracy increased 3%-9%.
- hyperspectral,
- hyperspectral classification,
- kernel method,
- minimum noise fraction

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