New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images

• Published in: IEEE transactions on signal processing Vol. 67; no. 2; pp. 520 - 534
• Main Authors: Mian, Ammar, Ginolhac, Guillaume, Ovarlez, Jean-Philippe, Atto, Abdourrahmane Mahamane
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Change detection; Computer simulation; Constant false alarm rate; Covariance matrices; Data models; Engineering Sciences; generalised likelihood ratio test; Image detection; Image resolution; Image time series; Likelihood ratio; Probability density function; Radar data; Radar detection; Radar imaging; Radar remote sensing; robust detection; Signal and Image processing; spherically invariant random vectors; Statistical tests; Statistics; Synthetic aperture radar; Time series; Time series analysis; RAPPORT DE VRAISEMBLANCE GENERALISE; Spherically Invariant Random Vectors; Generalised Likelihood Ratio Test; IMAGE TIME SERIES; Index Terms-Image Time Series; Syn- thetic Aperture Radar; VECTEURS ALEATOIRES SPHERIQUEMENT INVARIANTS; Change Detection; SYNTHETIC APERTURE RADAR; IMAGERIE A SYNTHESE D'OUVERTURE; DETECTION ROBUSTE; DETECTION DE CHANGEMENT; Robust detection; SERIE D'IMAGES SAR
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2018.2883011

This paper explores the problem of change detection in time series of heterogeneous multivariate synthetic aperture radar images. Classical change detection schemes have modeled the data as a realization of Gaussian random vectors and have derived statistical tests under this assumption. However, when considering high-resolution images, the heterogeneous behavior of the scatterers is not well described by a Gaussian model. In this paper, the data model is extended to spherically invariant random vectors where the heterogeneity of the images is accounted for through a deterministic texture parameter. Then, three separate detection problems are considered and generalized likelihood ratio test technique is used to derive statistical tests for each problem. The constant false alarm rate property of the new statistics are studied both theoretically and through simulation. Finally, the performance of the new statistics are studied both in simulation and on real synthetic aperture radar data and compared to Gaussian-derived ones. The study yields promising results when the data are heterogeneous.