NORCAMA: Change Analysis in SAR Time Series by Likelihood Ratio Change Matrix Clustering

Abstract : This paper presents a likelihood ratio test based method of change detection and classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on chAnge criterion MAtrix (NORCAMA). This method involves three steps: 1) multi-temporal pre-denoising step over the whole image series to reduce the effect of the speckle noise; 2) likelihood ratio test based change criteria between two images using both the original noisy images and the denoised images; 3) change classification by a normalized cut based clustering-and-recognizing method on change criterion matrix (CCM). The experiments on both synthetic and real SAR image series show the effective performance of the proposed framework.
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https://hal.archives-ouvertes.fr/hal-00997786
Contributeur : Xin Su <>
Soumis le : mercredi 28 mai 2014 - 18:16:06
Dernière modification le : samedi 17 septembre 2016 - 01:23:36
Document(s) archivé(s) le : jeudi 28 août 2014 - 13:11:18

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Xin Su, Charles-Alban Deledalle, Florence Tupin, Hong Sun. NORCAMA: Change Analysis in SAR Time Series by Likelihood Ratio Change Matrix Clustering. 2014. 〈hal-00997786〉

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