Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features

Abstract

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 hyperspectral 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.

Keywords

• edge preserving features (epfs)
• hyperspectral image (hsi) classification
• minimum noise fraction (mnf)
• principal component analysis (pca)
• support vector machine (svm)