TY - GEN
T1 - Effects of Unbalanced Data on Radiometric Transforming Model Fitting for Relative Radiometric Normalization
AU - Gan, Wenxia
AU - Geng, Jing
AU - Wang, Yu
AU - Xu, Jinying
AU - Yu, Weihang
AU - Yuan, Huanning
AU - Qin, Rongjun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - 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.
AB - 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.
KW - radiometric transforming model
KW - relative radiometric normalization
KW - stochastic simulations
KW - unbalanced data
UR - http://www.scopus.com/inward/record.url?scp=85102005883&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9324679
DO - 10.1109/IGARSS39084.2020.9324679
M3 - Conference contribution
AN - SCOPUS:85102005883
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2316
EP - 2319
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -