Change-Detection Using Contextual Information and Fuzzy Entropy Principle

Abstract

This paper presents an unsupervised change detection method for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modeling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. The fuzzy visualization of areas undergoing changes can be incorporated into a decision support system for prioritization of areas requiring environmental monitoring. One of the main problems related to unsupervised change detection methods lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both, the accuracy and the reliability of the change-detection process. To overcome such drawbacks, in this paper, we propose an automatic technique for the analysis of the difference image. Such technique allows the automatic selection of the decision threshold. We used a thresholding approach by performing fuzzy partition on a twodimensional (2-D) histogram, which included contextual information, based on fuzzy relation and maximum fuzzy entropy principle. Experimental results confirm the effectiveness of proposed technique.