文章基本信息

标题：KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION
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作者：Yang Bai ; Ping Tang ; Changmiao Hu 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2016
卷号：III-1
页码：49-53
DOI：10.5194/isprs-annals-III-1-49-2016
出版社：Copernicus Publications
摘要：The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization
关键词：Relative radiometric normalization; multivariate alteration detection (MAD); canonical correlation analysis (CCA); kernel version of canonical correlation analysis (KCCA)