Test statistics for synthetic aperture radar coherent change detection

Coherent change detection using paired synthetic aperture radar images is typically performed using a classical estimator of coherence applied under an assumption of complex Gaussian data. The magnitudes of the resultant coherence estimates are plotted as an image and used to gauge changes in the observed scene. Here we investigate the suitability of an alternative coherence estimator that further assumes the variances of the populations underlying each paired sample to be equal. We show experimentally that this alternative estimator outperforms the classical estimator even when the underlying variances are not equal, as long as they are close enough. We demonstrate the suitability of this estimator directly on publicly available synthetic aperture radar data, with a performance improvement observed through increased contrast in the corresponding coherent change detection images.