LMedS-Based Power Regression: An Optimal and Automatic Method of Radiometric Intercalibration for DMSP-OLS NTL Imagery

The further scientific applications of DMSP-OLS night-time light (NTL) imagery have been being limited by the accuracy, automation, and speed of radiometric intercalibration. In order to solve the aforementioned problems, this article is the first to propose a new least-median-of-squares (LMedS)-based power regression (LBPR) for automatically radiometric intercalibration and investigate the reasons for the optimal model of radiometric intercalibration, especially those based on the Taylor expansion and probability principle. NTL data in six regions all over the world, from 1994 and 1997 to 2007, were used as the test datasets. When the five kinds of LMedS-based radiometric intercalibration models (i.e., linear, quadratic, power, exponential, and logarithmic regression) are synthetically compared in absolute accuracy (adjusted RMSE) and running speed, it is concluded that the LBPR, which has the highest accuracy and preferable running speed, is recommended as the optimal method, which can also be used as a reference for other types of imagery preprocessing.