ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 49-53, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
01 Jun 2016
Yang Bai1,2, Ping Tang2, and Changmiao Hu2 1University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, China
2Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.20 Datun Road, Beijing, China
Keywords: Relative radiometric normalization, multivariate alteration detection (MAD), canonical correlation analysis (CCA), kernel version of canonical correlation analysis (KCCA) Abstract. 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.
Conference paper (PDF, 1429 KB)

Citation: Bai, Y., Tang, P., and Hu, C.: KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 49-53,, 2016.

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