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Proceedings Paper

Remote sensing image change detection research based on multiple classifiers fusion algorithm with fuzzy integral
Author(s): Yugang Tian; Yan Song; Xiaoxia Jia; Donghua Qin
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Paper Abstract

In this paper, we propose a novel change detection method. Multiple classifiers fusion combine results from various simple changes detection methods to improve change detection accuracy. In detail, we make use of multiple classifiers fusion based on fuzzy integrals for change detection. If the fuzzy measures are well defined, the accuracy of change detection can be improved distinctly. In this paper, we determine the fuzzy measures based on the Genetic Algorithm (GA). Though multiple classifiers fusion has robust performance, the input change detection result is still important. We review proposed pre-classification change detection method, and propose two contextual Fuzzy-C Means (FCM) algorithms and the Self Organization Feature Map (SOFM) change detection method. We select multi-spectral TM and pan SPOT image pairs as test data and apply five different change detection methods. The first experiment shows that different methods will produce different change detection accuracy, and different methods will complement each other. In addition, we apply fuzzy integral aided by genetic algorithm for combining different detection methods. The final experiment shows that our proposed method can improve change detection accuracy and has better performance than single detection method.

Paper Details

Date Published: 30 October 2009
PDF: 9 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941J (30 October 2009); doi: 10.1117/12.833445
Show Author Affiliations
Yugang Tian, China Univ. of Geosciences (China)
Yan Song, China Univ. of Geosciences (China)
Xiaoxia Jia, Wuhan Geotechnical Engineering and Surveying Institute (China)
Donghua Qin, China Univ. of Geosciences (China)


Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing

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