A New Detection Algorithm for Coherent Scatterers in SAR Data

In contrast to the random nature of synthetic aperture radar (SAR) data, it is also possible to identify bright targets whose scattering properties scarcely vary within imaging and time. These targets are commonly named point-like scatterers and can be found in both urban and natural environments. Permanent-scatterer interferometry techniques single out stable scatterers in a stack of SAR images, which preserve their backscattering stability along time. However, this methodology may not be optimum in natural scenarios, where the temporal stability of the scattering is rather reduced, or when the number of available SAR acquisitions is significantly small. Consequently, alternative methods have come out to detect stable scatters in a single SAR image, thus reducing all constraints related to their temporal behavior. Particularly, spectral diversity techniques are exploited to detect the so-called coherent scatterers. In this paper, a new detection scheme based on the generalized likelihood ratio test approach (GLRTA) is proposed, and its performance is extensively evaluated compared with three of the traditional methods, namely, the sublook coherence approach, the sublook entropy approach, and the phase variance approach. Remarkably, the GLRTA exploits both amplitude and phase information and does not need any further averaging (apart from sublooking processing with reduced signal bandwidth). The presented analysis is conducted both theoretically and with simulated data. For all scenarios, the new detector outperforms the other methods. The obtained results are validated also on real data. Finally, the proposed GLRTA is tested over different scattering scenarios, considering three TerraSAR-X acquisitions.