  This article investigates the potential of nonlocally filtered pursuit
monostatic TanDEM-X data for coastline detection in comparison to conventional
TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For
this task, an unsupervised coastline detection procedure based on scale-space
representations and K-medians clustering as well as morphological image
post-processing is proposed. Since this procedure exploits a clear
discriminability of "dark" and "bright" appearances of water and land surfaces,
respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data
acquired in pursuit monostatic mode is expected to provide a promising benefit.
In addition, we investigate the benefit introduced by a utilization of a
non-local InSAR filter for amplitude denoising and coherence estimation instead
of a conventional box-car filter. Experiments carried out on real TanDEM-X
pursuit monostatic data confirm our expectations and illustrate the advantage
of the employed data configuration over conventional TanDEM-X products for
automatic coastline detection.
