Reweighted Kernel-Based Nonlinear Hyperspectral Unmixing With Regional l1-Norm Regularization

Improving the performance of nonlinear unmixing has become an active topic among the remote sensing applications. Usually, the noise levels of hyperspectral images (HSIs) vary with different bands. However, this fact is generally ignored and may, to some extent, result in a degradation of the unmixing results. Nonetheless, valuable spatial information that provides a great potential for improving the performance has seldom been considered in the current nonlinear unmixing. In this letter, we propose a novel kernel-based nonlinear unmixing model in which the band-wise noise characterization and the spatial relationships of HSIs are incorporated to solve the above problems. Firstly, the noise levels of different bands are estimated based on the results of superpixel segmentation, and then they are used to characterize the roles of different bands in the unmixing process. To exploit the spatial relationships in the superpixels, a regional l(1)-norm regularization is proposed and incorporated into the unmixing model. Experimental results on both synthetic and real hyperspectral datasets demonstrate the superiority of the proposed model compared to the state-of-the-art nonlinear unmixing methods.

Automated summary

Improving the performance of nonlinear unmixing in remote sensing applications has become a popular topic. A novel kernel-based nonlinear unmixing model is proposed in this letter, incorporating band-wise noise characterization and spatial relationships of hyperspectral images to address existing problems and showing superior results compared to current methods.