Hyperspectral Intrinsic Image Decomposition With Enhanced Spatial Information

Abstract

Hyperspectral intrinsic image decomposition (HyperIID) has been proven to be a very useful approach to reduce the spectral uncertainty in the remote sensing imaging process and improve the classification. In this article, a new HyperIID with enhanced spatial information, called ESI-IID, is proposed to overcome the deficiency of low spatial resolution in the existing HyperIID methods. With the aid of high-resolution (HR) panchromatic (PAN) image, the proposed method embeds the HR spatial information into the intrinsic decomposition model and enhances spatial details of the intrinsic component. The proposed ESI-IID introduces three constraints: 1) we make the constraint on spectral information to protect it from distortion during the spatial resolution enhancement process; 2) we add the constraint on spatial information to make sure that the details of edges will be well kept; and 3) based on the assumption that the reflectance component has a strong correlation in the local neighborhood, we add the self-constraint on reflectance component, in which the similarity matrix consists of two parts extracted from hyperspectral images and PAN image, respectively. Finally, we build a matrix energy function according to the aforementioned constraints and solve it by finding the minimum Frobenius norm iteratively. Both visual and quantitative experiments on simulated and real datasets demonstrate that the proposed method outperforms other alternative methods with high reliability.