Robustness of Change Detection Algorithms in the Presence of Registration Errors

Abstract

Accurate registration of multi-temporal remote sensing images is critical to any change detection study. The presence of registration errors in the images may affect the accuracy of change detection. In this paper, we evaluate the performance of two change detection algorithms in the presence of artificially introduced registration errors in the dataset. The algorithms considered are image differencing and an algorithm based on a Markov random field (MRF) model. Registration errors have been introduced in four different ways: only in x direction, only in y direction, in both x and y directions without any rotational misregistration, and finally in both x and y directions together with rotational misregistration. Three temporal datasets, a simulated dataset and two synthetic datasets created from remote sensing images acquired by the Landsat TM sensor, have been used in our study. The results indicate that the change detection algorithm based on the MRF model is more robust to the presence of registration errors than the image differencing method.