Mining remote sensing image data: an integration of fuzzy set theory and image understanding techniques for environmental change detection

This paper presents an image understanding approach to mine remotely sensed image data from different source dates for environmental change detection. It is focused on the immediate needs for knowledge discovery from large sets of image data for environmental monitoring. In contrast to the traditional approaches for change detection, we introduce a wavelet-based hierarchical scheme which integrates fuzzy set theory and image understanding techniques for knowledge discovery of the remote image data. The proposed approach includes algorithms for hierarchical change detection, region representations and classification. The effectiveness of the proposed algorithms is demonstrated throughout the completion of three tasks, namely hierarchial detection of change by fuzzy post classification comparisons, localization of change by B-spline based region representation, and categorization of change by hierarchial texture classification.