Weighted normalized mutual information based change detection in remote sensing images

Change detection from remote sensing images is getting more interest now a days because of abrupt changes in earth surface due to natural disasters or man-made activities. So it's an important research question of how to extract relevant information about the changes due to rainfall, droughts, flooding, destroying land cover areas and so on. This problem has been studied in some research however many of these did not consider the nonlinear relationship while detecting the changes. In this research, above limitation has been addressed and Weighted Normalized Mutual Information (WNMI) is utilized for the improvement. The WNMI technique has been applied between the reference and target images to find out the changes. Thus the changes between every object of the given dataset have been identified and able to observe the damage of any specific area as well as its subsequent recovery. Weighting has been done to count significance at the pixel level. The proposed technique can detect the changes more effectively than the traditional mutual information approach. Experimental analysis is carried on real remote sensing images and it is found that the proposed method can detect more than 96% of changes which is much better than the standard benchmark techniques.