Online change detection in SAR time-series with Kronecker product structured scaled Gaussian models - Laboratoire d’Informatique, Systèmes, Traitement de l’Information et de la Connaissance
Article Dans Une Revue Signal Processing Année : 2024

Online change detection in SAR time-series with Kronecker product structured scaled Gaussian models

Résumé

We develop the information geometry of scaled Gaussian distributions for which the covariance matrix exhibits a Kronecker product structure. This model and its geometry are then used to propose an online change detection (CD) algorithm for multivariate image times series (MITS). The proposed approach relies mainly on the online estimation of the structured covariance matrix under the null hypothesis, which is performed through a recursive (natural) Riemannian gradient descent. This approach exhibits a practical interest compared to the corresponding offline version, as its computational cost remains constant for each new image added in the time series. Simulations show that the proposed recursive estimators reach the Intrinsic Cramér-Rao bound. The interest of the proposed online CD approach is demonstrated on both simulated and real data.
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Dates et versions

hal-04694905 , version 1 (11-09-2024)

Identifiants

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Ammar Mian, Guillaume Ginolhac, Florent Bouchard, Arnaud Breloy. Online change detection in SAR time-series with Kronecker product structured scaled Gaussian models. Signal Processing, 2024, 224, pp.109589. ⟨10.1016/j.sigpro.2024.109589⟩. ⟨hal-04694905⟩
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