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- W3133012945 abstract "Most of the existing (Relative Radiometric Normalization) RRN research focus on the automatic sample selection, while unbalanced data effect on the regression is not noted and the pre-selected sample set like the Pseudo-invariant Features (PIFs), or homogeneous pixels are directly used to solve the radiometric transforming models without considering the representativeness of the sample set. In this paper, we investigated the effects of unbalanced data, specifically on two aspects: 1) statistical properties of the estimated model parameters, 2) the normalizing accuracy of the fitted model. To make the work thoroughly, four regression methods are investigated, including Least Square Regression (LSQ), Theil-Sen estimator (TSR), Support vector machine regression (SVR), and Random forest regression (RFR). And Monte-Carlo Simulation is used to generated various sample sets with different distributions. It is demonstrated that the LSQ and the TSR are vulnerable to data unbalance, in terms of both the estimated model parameters and the normalizing accuracy of the fitted radiometric transforming model, whereas the SVR and RFR are not sensitive." @default.
- W3133012945 created "2021-03-01" @default.
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- W3133012945 date "2020-09-26" @default.
- W3133012945 modified "2023-10-18" @default.
- W3133012945 title "Effects of Unbalanced Data on Radiometric Transforming Model Fitting for Relative Radiometric Normalization" @default.
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- W3133012945 doi "https://doi.org/10.1109/igarss39084.2020.9324679" @default.
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