LIN Na, YANG Wunian, WANG Bin. Hyperspectral Image Feature Extraction via Kernel Minimum Noise Fraction Transform[J]. Geomatics and Information Science of Wuhan University, 2013, 38(8): 988-992.
Citation: LIN Na, YANG Wunian, WANG Bin. Hyperspectral Image Feature Extraction via Kernel Minimum Noise Fraction Transform[J]. Geomatics and Information Science of Wuhan University, 2013, 38(8): 988-992.

Hyperspectral Image Feature Extraction via Kernel Minimum Noise Fraction Transform

Funds: 国家自然科学基金资助项目(41071265); 高等学校博士学科点专项科研基金资助项目(20105122110006); 重庆市自然科学基金资助项目(cstc2012jjA40055); 国土资源部地学空间信息技术重点实验室开放基金资助项目(KLGSIT2013-03)
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  • Received Date: May 05, 2013
  • Revised Date: May 05, 2013
  • Published Date: August 04, 2013
  • Hyperspectral image linear feature extraction methods often cause information loss and distortion.In view of this,a new kernel minimum noise fraction(KMNF) transform hyperspectral image nonlinear feature extraction method is proposed that introduces a kernel method to minimum noise fraction(MNF) transform.Hyperspectral image KMNF feature extraction experiments were carried out.CUPRITE AVIRIS data experimental results show that sample number influences KMNF slightly,a small number of samples can get almost the same result as a large number of samples;KMNF feature extraction reflects the nonlinear characteristics of hyperspectral images,and endmember extraction effects based on KMNF images outweigh MNF images.
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