NOISE ROBUST HYPERSPECTRAL IMAGE CLASSIFICATION WITH MNF-BASED EDGE PRESERVING FEATURES

Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF) instead of PCA to reduce the dimensionality of the hyperspec-tral data cube to be classified. We keep all the rest steps from the PCA-based EPFs for HSI classification. Since MNF can preserve fine features of a HSI data cube better than PCA, our new method can outperform PCA-EPFs for HSI classification significantly. Experimental results show that our new method performs better than the PCA-based EPFs under such noisy environment as Gaussian white noise and shot noise. In addition, our MNF+EPFs outperform the PCA+EPFs even when no noise is added to the HSI data cubes for most testing cases, which is very desirable in remote sensing.