%0 Journal Article
%T Application of kernel independent component analysis in image processing<br>核独立成分分析在图像处理中的应用*
%A CHEN Min
%A JIANG Yun-fei
%A XI Xin
%A LIU Zhi-gang
%A <br>陈敏
%A 江云菲
%A 习鑫
%A 刘志刚a
%J 计算机应用研究
%D 2008
%I 
%X Basic principles of blind source separation technology and independent component analysis were presented. Principles of kernel functions and kernel independent component analysis were discussed. Furthermore, the algorithm of KICA based on kernel canonical correlation analysis was introduced in details. Simulation on one-dimensional mixed signals was presented to verify the superiority of KICA. The problem of ICA separation inherent uncertainty of the order and the magnitude of the results was indicated finally. Later experiments on natural images and remotely sensed images show that KICA can separate mixed images successfully, In addition, processed images by this approach can better reflect the land surface conditions.
%K kernel independent component analysis(KICA)
%K image processing
%K remotely sensed image<br>核独立成分分析
%K 图像处理
%K 遥感影像
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=BE2DFA620E3628961DFB61F01EEC954C&yid=67289AFF6305E306&vid=C5154311167311FE&iid=CA4FD0336C81A37A&sid=E42CAFB11D4BE21A&eid=D0182A31A5EB14BA&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=8