On the Detection and Long-Term Path Visualisation of A-68 Iceberg
The article presents a methodology for examining a temporal sequence of synthetic aperture radar (SAR) images, as applied to the detection of the A-68 iceberg and its drifting trajectory. Using an improved image processing scheme, the analysis covers a period of eighteen months and makes use of a se...
| Published in: | Remote Sensing |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
MDPI AG
2021
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| Subjects: | |
| Online Access: | https://doi.org/10.3390/rs13030460 https://doaj.org/article/b9b372a16cea4bd1a127a1cdbde39f3d |
| Summary: | The article presents a methodology for examining a temporal sequence of synthetic aperture radar (SAR) images, as applied to the detection of the A-68 iceberg and its drifting trajectory. Using an improved image processing scheme, the analysis covers a period of eighteen months and makes use of a set of Sentinel-1 images. A-68 iceberg calved from the Larsen C ice shelf in July 2017 and is one of the largest icebergs observed by remote sensing on record. After the calving, there was only a modest decrease in the area (about 1%) in the first six months. It has been drifting along the east coast of the Antarctic Peninsula, and is expected to continue its path for more than a decade. It is important to track the huge A-68 iceberg to retrieve information on the physics of iceberg dynamics and for maritime security reasons. Two relevant problems are addressed by the image processing scheme presented here: (a) How to achieve quasi-automatic analysis using a fuzzy logic approach to image contrast enhancement, and (b) The use of ferromagnetic concepts to define a stochastic segmentation. The Ising equation is used to model the energy function of the process, and the segmentation is the result of a stochastic minimization. |
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