Abstract
Recently, independent component analysis (ICA) has been used in hyperspectral imagery (HSI) unmixing. It considers the hyperspectral signal from a given pixel as a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources). This subpixel-based ICA model has two disadvantages. One is that it is difficult to choose the "mixed" pixels automatically, and the other is that the sources are not statistically independent. As the spatial resolution of HSI data grows higher, we introduce a new substance-based ICA model; that is, distinct substances are regarded as the original sources, and every band image is a linear mixture of them. However, ICA is a very general-purpose statistical technique — it does not take the spatial information into account while unmixing HSI. In this paper, the Markov random field (MRF) model is adopted to incorporate the spatial information into ICA. It is thought of as a powerful tool to model the joint probability distribution of the image pixels based on local spatial interactions. The experimental results demonstrate that the proposed MRF-ICA mixture model provides an effective approach for HSI unmixing.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Wavelets, Multiresolution and Information Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
