Spectral Super-Resolution by Using Universal and Private Jointed Spectral Library and Its Applications
Wei Leng, Xiaolin Han, Jingwei Deng, Huan Zhang, Wenqiang Li, Weidong Sun
Abstract
Spectral library based spectral super-resolution from high-spatial multispectral to hyperspectral image is one of the most efficient ways to obtain high-spatial hyperspectral satellite images, which can be used in various applications. Although most of the published universal spectral libraries can really provide reliable spectral information of the common ground objects, poor region specificity of the universal spectral libraries may limit the accuracy of spectral super-resolution and thus the subsequent applications. To address the above issue, this paper proposes a new spectral super-resolution method for high-spatial multispectral satellite images by using the universal and private jointed spectral library. Specifically, a private spectral library consisting of the spectra of interested ground objects is introduced to the universal spectral library, to form a new joint spectral library, and a new spectral super-resolution model by using the joint spectral library is constructed. Then, band matching between the desired high-spatial hyperspectral image and the joint spectral library will be carried out, to map the joint spectral library into a specific spectral library. After that, contributions of the two universal and private spectral dictionaries will be well balanced by a weighting factor under the sparse representation framework. And finally, the spectral dictionary and its related coefficients will be optimized by the alternating direction method of multipliers (ADMM). Comparison results with the relative state-of-the-art methods shown the superiority of this proposed method, and two typical applications of the tobacco and wheat classification will also be given to evaluate its effectiveness in practical applications.