Optimized kernel minimum noise fraction transformation for hyperspectral image classification

Lianru Gao, Bin Zhao, Xiuping Jia, Wenzhi Liao, Bing Zhang, Qi Wang (Editor)

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
14 Downloads (Pure)


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.
Original languageEnglish
Number of pages20
JournalRemote Sensing
Issue number6
Publication statusPublished - 1 Jun 2017


  • hyperspectral image
  • feature extraction
  • dimensionality reduction
  • optimized kernel minimum noise fraction (OKMNF)
  • discriminant analysis
  • operational method
  • component analysis
  • framework
  • selection
  • profiles
  • spaces
  • SVM


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