AN IMPROVED LOCAL SIMILARITY MEASURE ESTIMATION FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

Detecting changes in remote sensing images taken at different times is challenging when images' data come from different sensors. The performance of change detection algorithms based on radiometric values alone is not satisfactory and need the fusion of other features. Local similarity measures such as Mutual Information, Kullback-Leibler Divergence, and Cluster Reward Algorithm can be used for enhancing change detection. In the paper, we propose an improved local similarity measure using weighted local histogram. Each pixel contributes to the calculation of the histogram according to its weight only. The weight assigned to each pixel in the histogram estimation window follows an exponential function of its distance from the center of the window and the corresponding pixel value in an initial change map image which is derived from other micro-structure or radiometric information. The proposed improved similarity measure benefits from the good detection ability of small estimation window and the good estimation accuracy of large estimation window; hence it can replace the time-consuming multi-scale selection approaches for statistics based similarity measures in remote sensing. The efficiency of this useful improvement has been validated on change detection in remote sensing image series.