Coastline detection with time series of SAR images

Ao, Dongyang; Dumitru, Octavian; Schwarz, Gottfried; Datcu, Mihai

doi:10.1117/12.2278318

Cited by 8 publications

(5 citation statements)

References 12 publications

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confidence: 99%

“…The Sentinel-1 dual-polarization characteristics are also employed in unsupervised methods, based on the correlation between copolarized and cross-polarized channels, to delineate the shorelines [18], [19]. The derived coastline can be used to assess temporal changes by applying the proposed methods [19], [20] to individual images from Sentinel-1 time series. The accuracy achieved by state-of-the-art algorithms is generally estimated through comparisons with manually traced coastlines or ancillary information, including global positioning system data.…”

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confidence: 99%

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Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications

Pelich

Chini

Hostache

et al. 2021

IEEE Geosci. Remote Sensing Lett.

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This letter addresses the use of the Sentinel-1 time series with the aim of proposing an automatic and unsupervised coastline detection method that averages the dynamical variations of coastal areas over a limited period of time, e.g., one year. First, we propose applying a temporal averaging filter that allows the temporal variations in coastal areas, e.g., due to tides or vegetation, to be encapsulated, and, at the same time, the speckle to be reduced, without decreasing the spatial resolution of the synthetic aperture radar (SAR) time series. Then, based on the distinctive backscattering values of the sea and land pixels, we will employ an iterative hierarchical tiling method in order to accurately characterize the two classes using bimodal distribution. The distribution is then segmented by a thresholding and regiongrowing procedure to separate the sea and land classes. A largescale quantitative comparison between the SAR-derived and open street map (OSM) coastlines allows for a numerical evaluation of the results, i.e., an overall agreement ranging from 80% to 90%. In addition, Sentinel-2 images are used to evaluate the estimated SAR coastline qualitatively. Furthermore, the benefits of having an accurate SAR coastline are shown in the case of two well-known Earth observation-monitoring applications, ship detection, and floodwater mapping.

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confidence: 99%

Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications

Pelich

Chini

Hostache

et al. 2021

IEEE Geosci. Remote Sensing Lett.

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“…Edge detection methods leverage the gradient changes along image edges to extract coastlines. Commonly employed techniques include Sobel's algorithm [12], Laplace's algorithm, and Canny operator methods [13][14][15][16]. Moreover, Wang et al [17] proposed a robust ridge and beach tracking method that exploits the statistical characteristics of land and sea pixel intensities to delineate their boundaries.…”

Section: Introductionmentioning

confidence: 99%

A Shoreline Extraction Method Based on Double-Loop Network Framework

Li,

Cao,

et al. 2023

Preprint

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Satellite remote sensing-based shoreline extraction can provide great supports for scientific planning and utilization of coastal zones, marine ecological protection, and efficient shoreline management. In this paper, based on the designed double-loop network framework, we propose an adaptive image segmentation algorithm to achieve end-to-end shoreline extraction. Specifically, by considering triple constraints, a label reassignment module is firstly designed to enabling the extraction network with self-supervised learning ability for adaptive segmentation. Secondly, a data stream attention scheme is constructed to guide the network’s learning directionally, which could enhance the robustness and convergence speed of the double-loop network. Finally, a series of experiments are carried out to validate the effectiveness and feasibility of our proposed work.

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“…The edge-based methods utilize the distinctive edge feature from the abrupt transition. The common methods including Sobel, Roberts (Yang et al, 2018), Laplacian, and Canny operators (Lin et al, 2013;Paravolidakis et al, 2016;Ao et al, 2017;Widyantara et al, 2017;Paravolidakis et al, 2018) can be adopted to extract the waterline. Wang and Liu (2019) proposed a robust ridge-tracing method utilizing the statistical properties of the pixel intensities in the land and sea to detect the boundary.…”

Section: Introductionmentioning

confidence: 99%

Waterline Extraction for Artificial Coast With Vision Transformers

Yang

Wang

Zhai

2022

Front. Environ. Sci.

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Accurate acquisition for the positions of the waterlines plays a critical role in coastline extraction. However, waterline extraction from high-resolution images is a very challenging task because it is easily influenced by the complex background. To fulfill the task, two types of vision transformers, segmentation transformers (SETR) and semantic segmentation transformers (SegFormer), are introduced as an early exploration of the potential of transformers for waterline extraction. To estimate the effects of the two methods, we collect the high-resolution images from the web map services, and the annotations are created manually for training and test. Through extensive experiments, transformer-based approaches achieved state-of-the-art performances for waterline extraction in the artificial coast.

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Coastline detection with time series of SAR images

Cited by 8 publications

References 12 publications

Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications

Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications

A Shoreline Extraction Method Based on Double-Loop Network Framework

Waterline Extraction for Artificial Coast With Vision Transformers

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