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Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

Author:
Schmitt, Michael, Baier, Gerald, Zhu, Xiao Xiang
Source:
ISPRS journal of photogrammetry and remote sensing 2019 v.148 pp. 130-141
ISSN:
0924-2716
Subject:
coasts, data collection, image analysis
Abstract:
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.
Agid:
6265664