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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
Contributor : Xin Su <>
Submitted on : Wednesday, May 28, 2014 - 6:16:06 PM
Last modification on : Friday, October 16, 2020 - 12:02:52 AM
Long-term archiving on: : Thursday, August 28, 2014 - 1:11:18 PM

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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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