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Proceedings Paper

Mining remote sensing image data: an integration of fuzzy set theory and image understanding techniques for environmental change detection
Author(s): Peter W. Eklund; Jane You; Peter Deer
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Paper Abstract

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.

Paper Details

Date Published: 6 April 2000
PDF: 8 pages
Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381741
Show Author Affiliations
Peter W. Eklund, Griffith Univ. (Australia)
Jane You, Griffith Univ. (Australia)
Peter Deer, Defence Science and Technology Organisation (Australia)


Published in SPIE Proceedings Vol. 4057:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology II
Belur V. Dasarathy, Editor(s)

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