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

## TL;DR

This study explores the use of nonlocally filtered pursuit monostatic TanDEM-X data for improved automatic coastline detection, demonstrating advantages over conventional data through experiments on real datasets.

## Contribution

It introduces an unsupervised coastline detection method leveraging nonlocally filtered pursuit monostatic TanDEM-X data, highlighting its benefits over traditional approaches.

## Key findings

- Nonlocally filtered pursuit monostatic data improves coastline detection accuracy.
- The proposed method outperforms conventional TanDEM-X products in experiments.
- Utilizing amplitude and coherence imagery enhances discriminability of land and water surfaces.

## 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.