Optimized kernel minimum noise fraction transformation for hyperspectral image classification

Gao, Lianru and Zhao, Bin and Jia, Xiuping and Liao, Wenzhi and Zhang, Bing (2017) Optimized kernel minimum noise fraction transformation for hyperspectral image classification. Remote Sensing, 9 (6). ISSN 2072-4292

[thumbnail of Gao-etal-RS2017-Optimized-kernel-minimum-noise-fraction-transformation-hyperspectral-image-classification]
Text (Gao-etal-RS2017-Optimized-kernel-minimum-noise-fraction-transformation-hyperspectral-image-classification)
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (8MB)| Preview


    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.