Sometimes, the abundance in hyperpectral images is really a nonlinear function of different elemental abundances. Hence there is the need for the nonlinear dictionaries mentioned yesterday. Now we just need to have sensors that help in that deconvolution.
Nonlinear estimation of material abundances in hyperspectral images with L1-norm spatial regularization. by Chen, Jie, Richard, Cédric, and Honeine, Paul
Integrating spatial information into hyperspectral unmixing procedures has been shown to have a positive effect on the estimation of fractional abundances due to the inherent spatial–spectral duality in hyperspectral scenes. However, current research works that take spatial information into account are mainly focused on the linear mixing model. In this paper, we investigate how to incorporate spatial correlation into a nonlinear abundance estimation process. A nonlinear unmixing algorithm operating in reproducing kernel Hilbert spaces, coupled with a 1-type spatial regularization, is derived. Experiment results, with both synthetic and real hyperspectral images, illustrate the effectiveness of the proposed scheme.
Of related interest:
- Detection of nonlinear mixtures using Gaussian processes: Application to hyperspectral imaging. by Imbirida, Tales, Bermudez, Jose-Carlos M., Tourneret, Jean-Yves, and Richard, Cédric
- Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm. by Gao, Wei, Chen, Jie, Richard, Cédric, and Huang, Jianguo
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