Parallel relative radiometric normalisation for remote sensing image mosaics

Relative radiometric normalisation (RRN) is a vital step to achieve radiometric consistency among remote sensing images. Geo-analysis over large areas often involves mosaicking massive remote sensing images. Hence RRN becomes a data-intensive and computing-intensive task. This study implements a parallel RNN method based on the iteratively re-weighted multivariate alteration detection (IR-MAD) transformation and orthogonal regression. To parallelise the method of IR-MAD and orthogonal regression, there are two key problems: the normalisation path determination and the task dependence on normalisation coefficients calculation. In this paper, the reference image and normalisation paths are determined based on the shortest distance algorithm to reduce normalisation error. Formulas of orthogonal regression are acquired considering the effect of the normalisation path to reduce the task dependence on the calculation of coefficients. A master-slave parallel mode is proposed to implement the parallel method, and a task queue and a process queue are used for task scheduling. Experiments show that the parallel RRN method provides good normalisation results and favourable parallel speed-up, efficiency and scalability, which indicate that the parallel method can handle large volumes of remote sensing images efficiently.