Spectral-Spatial Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

This paper proposes a new spectral-spatial kernel minimum noise fraction transformation (SS-KMNF) as a dimensionality reduction method for hyperspectral image (HSI) classification. The kernel minimum noise fraction (KMNF) method generates new components ordered by image quality and the key index of image quality is noise fraction. In SS-KMNF, the high correlation between bands in homogeneous regions obtained using a superpixel technique is applied to improve the precision of noise fraction. Compared with the original KMNF, SS-KMNF can fully use spectral and spatial information contained in a HSI and is more effective in enhancing the performance of dimensionality reduction for HSI classification. Moreover, a new classification strategy is proposed based on superpixels which are considered as the basic unit instead of pixels for classifying features extracted by SS-KMNF. Experimental results show that SS-KMNF can get better dimensionality reduction performance than KMNF, and the superpixel is not only beneficial to precisely estimating noise fraction but can also increase the classification accuracy of features extracted by SS-KMNF. In addition, SS-KMNF can also be applied to classify transmission lines in electrical power systems.