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Abstract
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.
Highlights
Hyperspectral images provide very rich spectral information of earth objects [1,2]
To overcome the above limitations, we propose a new framework to optimize kernel minimum noise fraction (KMNF) (OKMNF)
This section designs three experiments to evaluate the performances of a few noise estimation algorithms and dimensionality reduction methods
Summary
Hyperspectral images provide very rich spectral information of earth objects [1,2]. A hyperspectral image contains hundreds of spectral bands with high spectral resolution. The high dimensionality reduces the efficiency of hyperspectral data processing. In hyperspectral image classification, another problem is known as the curse of dimensionality or the Hughes phenomenon [3]. The more spectral bands the image has, the more training samples are needed in order to achieve an acceptable classification accuracy. Dimensionality reduction is a very effective technique to solve this problem [5,6]. Dimensionality reduced data should well represent the original data, and can be considered as the extracted features for classification [7,8,9]. When the data dimensionality is Remote Sens. When the data dimensionality is Remote Sens. 2017, 9, 548; doi:10.3390/rs9060548 www.mdpi.com/journal/remotesensing
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