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Proceedings Paper

Unsupervised change detection on SAR images using Markovian fusion
Author(s): Keming Chen; Chunlei Huo; Jian Cheng; Zhixin Zhou; Hanqing Lu
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Paper Abstract

In this paper, we present a novel unsupervised change detection approach in temporal sets of synthetic aperture radar (SAR) images using Markovian fusion. This method is carried out within a Markovian framework which combines two different change detection algorithms to achieve noise removing and spatial information preserving at the same time. This approach is composed of two steps: 1) two change maps are generated by two distinctive but complementary approaches respectively; 2) final results are achieved by fusing the two change maps within a Markovian framework. In the first step, two different thresholding algorithms are selected to get two change maps aimed at speckle noise removing and spatial contexture preserving respectively; In the second step, a solution to fusion the two change maps through a Markov random field framework is proposed. The minimization of energy function is carried out through iterative conditional mode (ICM) algorithm because of its simplicity and moderate computation-consuming. Experiments results obtained on a SAR data set confirm the effectiveness of the proposed approach. It shows that the fusion approach based on MRFs model is a promising way of achieving robust unsupervised change detection.

Paper Details

Date Published: 14 November 2007
PDF: 8 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67901S (14 November 2007); doi: 10.1117/12.749309
Show Author Affiliations
Keming Chen, Institute of Automation (China)
Chunlei Huo, Institute of Automation (China)
Jian Cheng, Institute of Automation (China)
Zhixin Zhou, Institute of Automation (China)
Beijing Institute of Remote Sensing (China)
Hanqing Lu, Institute of Automation (China)

Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications
Yongji Wang; Jun Li; Bangjun Lei; Chao Wang; Liang-Pei Zhang; Jing-Yu Yang, Editor(s)

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