Estimation of signal-dependent and -independent noise from hyperspectral images using a wavelet-based superpixel model

Abstract

ABSTRACTNoise estimation is crucial for many hyperspectral (HS) image processing algorithms. In real HS images, the random noise is mainly composed of a signal-dependent (SD) photon noise component and a signal-independent (SI) electronic noise component. Based on a parametric model that accounts for the dependence of noise variance on the useful image signal, a novel method is proposed to estimate SD and SI noise variances in this paper. In order to accurately detect the homogeneous regions in noisy images, a new wavelet-based superpixel model is designed to segment a HS images into small patches that adhere to the local textures and hence persist in homogeneous characteristic. Then, the relevance vector machine (RVM) is exploited to split the noise and useful image signal in homogeneous superpixels. Finally, the SD and SI noise variances are obtained by fitting the scatter points of local means versus local total noise variances. Experiments on synthetic and real airborne visible/infrared imaging spectrometer (AVIRIS) HS images demonstrate the effectiveness of the proposed method.