Histogram thresholding for unsupervised change detection of remote sensing images

Abstract

The change-detection problem can be viewed as an unsupervised classification problem with two classes corresponding to changed and unchanged areas. Image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vectors associated with each pixel in the study area. To separate out the changed and unchanged classes in the difference image automatically, any unsupervised technique can be used. Thresholding is one of the cheapest techniques among them. However, in thresholding approaches, selection of the best threshold value is not a trivial task. In this work, several non-fuzzy and fuzzy histogram thresholding techniques are investigated and compared for the change-detection problem. Experimental results, carried out on different multitemporal remote sensing images (acquired before and after an event), are used to assess the effectiveness of each of the thresholding techniques. Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques.

Acknowledgements

The authors thank the anonymous referees for their constructive criticism and valuable suggestions. The authors thank the Department of Science and Technology, Government of India and University of Trento, Italy, the sponsors of the ITPAR program and Prof. L. Bruzzone, the Italian collaborator of this project, for providing the data.