Abstract
The kernel minimum noise fraction (KMNF) method is a nonlinear dimensionality reduction method for hyperspectral images. KMNF can transform the original data into higher dimensional feature space by using nonlinear transformation project. The key issue of KMNF is the noise estimation. The original KMNF performs noises estimation based on spatial neighborhood information. However, the spatial resolution of hyperspectral images always is not very high, and the images usually have seriously mixed pixels. Therefore, the spatial information is not enough to precisely estimate noise for KMNF. Differently, we adopt spectral correlation information which is more stable to estimate noise for KMNF. The proposed method is named the optimized KMNF method (OKMNF). Experimental results using real hyperspectral dataset demonstrate that OKMNF has much better performance than KMNF.
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