A Cross-Calibration Approach for Coastline Extraction With Time Series Satellite Images
In tidal flat regions, the spectral differences between water and land are often weak, making it challenging to extract the coastline from remote sensing imagery. Due to tidal influences, the satellite images captured at different times have different water coverage in nearshore area. Images at high...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11053792/ |
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| Summary: | In tidal flat regions, the spectral differences between water and land are often weak, making it challenging to extract the coastline from remote sensing imagery. Due to tidal influences, the satellite images captured at different times have different water coverage in nearshore area. Images at high tide typically display more distinct spectral characteristics between water and land. This study proposes a coastline optimization approach that utilizes isobath extracted from high-tide image to optimize the coastline extracted from low-tide image, thereby addressing the problem of weak spectral separability. The isobath, extracted from bathymetric inversion maps in steep slope areas during high tide, is referred to as the relative 0-meter isobath. The steep slope identification algorithm (SSI-A) is employed to detect these areas. The relative 0-meter isobath is identified in these areas. It is then used to optimize the coastline at an ebb tide moment, enhancing both the accuracy and consistency of coastline extraction. As a case study, the method is applied to Jibei Island, located in the northern part of the Penghu Islands. Based on different sensor images captured at different times, the proposed approach is used to optimize the coastline extracted by NDWI and SVM. The proposed approach reduces the average error by 13 meters based on Sentinel-2 with 10 m spatial resolution, and by 18 meters based on Landsat-8 and EnMAP image with 30 m spatial resolution. This approach provides more reliable data support for nearshore topographic evolution studies and coastal monitoring. |
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| ISSN: | 2169-3536 |