Two-Stage Change Detection for Synthetic Aperture Radar

Abstract

Coherent change detection using paired synthetic aperture radar images is often performed using a classical coherence estimator that is invariant to the true variances of the populations underlying each paired sample. While attractive, this estimator is biased and requires a significant number of samples to yield good performance. Increasing sample size often results in decreased image resolution. Thus, we propose use of Berger's coherence estimate because with the same number of pixels, the estimator effectively doubles the sample support without sacrificing resolution when the underlying population variances are equal or near equal. A potential drawback of this approach is that it is not invariant since its distribution depends on the pixel pair population variances. While Berger's estimator is inherently sensitive to the inequality of population variances, we propose a method of insulating the detector from this acuity. A two-stage change statistic is introduced to combine a non-coherent intensity change statistic given by the sample variance ratio followed by the alternative Berger estimator which assumes equal population variances. The first stage detector identifies pixel pairs that have non-equal variances as changes caused by the displacement of sizable object. The pixel pairs that are identified to have equal or near equal variances in the first stage are used as an input to the second stage. The second stage test uses the alternative Berger coherence estimator to detect subtle changes such as tire tracks and footprints. We show experimentally that the proposed method yields higher contrast SAR change detection images This work is sponsored by the United States Air Force under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the United States Government.

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