Decomposition of SAR images'' mixed pixels based on supervised learning ICA algorithm
基于SL-ICA算法的SAR图像混合像元分解

Forresolving theproblem thatthere are lotsofmixed pixels in the SyntheticApertureRadar(SAR) images, againstthe flaw that the traditional IndependentComponentAnalysis(ICA) can not solve the decomposition ofmixed pixels effectively, we propose a new algorithm: Supervised Learning ICA algorithm(SL-ICA). Adding supervised learning restrictive conditions to the negentropy objective function, we implementnegentropy and restrictive conditions in a unified objective function, whichminimizes the errorwhilemaximizing the negentropy. At the same time, we optimize the objective function using a new dual-gradientdescent algorithm iteratively, which accelerates the computing speed. By testing SL-ICA and PrincipalComponentAnalysis (PCA). on artificial simulated SAR images and ENVISAT-ASAR (Advanced SyntheticApertureRadar) images ofBeijing, the results show thatSL-ICA can getmore precise results than the PCA.