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Remote Sens. 2017, 9(6), 548;

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Engineering and Information Technology, The University of New South Wales, Canberra Campus, Bruce ACT 2006, Australia
Department of Telecommunications and Information Processing, Ghent University, Ghent 9000, Belgium
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 27 April 2017 / Revised: 24 May 2017 / Accepted: 26 May 2017 / Published: 1 June 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [8345 KB, uploaded 5 June 2017]


This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy. View Full-Text
Keywords: hyperspectral image; feature extraction; dimensionality reduction; optimized kernel minimum noise fraction (OKMNF) hyperspectral image; feature extraction; dimensionality reduction; optimized kernel minimum noise fraction (OKMNF)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gao, L.; Zhao, B.; Jia, X.; Liao, W.; Zhang, B. Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification. Remote Sens. 2017, 9, 548.

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