Efficient Clustering-Based Noise Covariance Estimation for Maximum Noise Fraction

Abstract

Most hyperspectral images (HSI) have important spectral features in specific combination of wave numbers or channels. Noise in these specific channels or bands can easily overwhelm these relevant spectral features. Maximum Noise Fraction (MNF) by Green et al. [1] has been extensively studied for noise removal in HSI data. The MNF transform maximizes the Signal to Noise Ratio (SNR) in feature space, thereby explicitly requiring an estimation of the HSI noise. We present two simple and efficient Noise Covariance Matrix (NCM) estimation methods as required for the MNF transform. Our NCM estimations improve the performance of HSI classification, even when ground objects are mixed. Both techniques rely on a superpixel based clustering of HSI data in the spatial domain. The novelty of our NCM’s comes from their reduced sensitivity to HSI noise distributions and interference patterns. Experiments with both simulated and real HSI data show that our methods significantly outperforms the NCM estimation in the classical MNF transform, as well as against more recent state of the art NCM estimation methods. We quantify this improvement in terms of HSI classification accuracy and superior recovery of spectral features.