Hyperspectral and Multispectral Data Fusion by a Regularization Considering

Abstract

A hyperspectral (HS) image has high spectral resolution information but low spatial resolution information. To get an HS image of high spatial and spectral resolution (high-spatial HS image), fusion techniques are actively studied, which synthesize an HS image of low spatial and high spectral resolution and a multispectral (MS) image. The techniques can generate a high-spatial HS image by exploiting the high spectral and spatial resolution information of HS and MS images, respectively. However, the methods do not evaluate the edge similarity between generated HS and observed MS images, and do not denoise the MS image. As a result, when an observed MS image is noisy, these methods produce artifacts and spectral distortion. To tackle this problem, we propose a new HS and MS data fusion method using a hybrid spatio-spectral total variation (HSSTV), which is a regularization for HS image restoration. The method not only generates a high-spatial HS image but also denoises a given MS image, so that we obtain a high-spatial HS image even if the observed images are contaminated by noise. In the experiments, we demonstrate the advantages of our method over existing fusion methods and the effectiveness of HSSTV for MS image restoration.