Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)

Abstract

Radiometric normalization, as an essential step for multi-source and multi-temporal data processing, has received critical attention. Relative Radiometric Normalization (RRN) method has been primarily used for eliminating the radiometric inconsistency. The radiometric transforming relation between the subject image and the reference image is an essential aspect of RRN. Aimed at accurate radiometric transforming relation modeling, the learning-based non-linear regression method, Support Vector machine Regression (SVR) is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN. To evaluate the effectiveness of the proposed method, a series of experiments are performed, including two synthetic data experiments and one real data experiment. And the proposed method is compared with other methods that use linear regression, Artificial Neural Network (ANN) or Random Forest (RF) for radiometric transforming relation modeling. The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.

Keywords

• support vector machine regression (svr)
• non-linear
• radiometric transforming relation
• relative radiometric normalization (rrn)
• multi-source data