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Introduction
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Publications
Publications (8)
Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only...
Coasts are continually changing and remote sensing from satellite has the potential to both map and monitor coastal change at multiple scales. This study aims to assess the application of shorelines extracted from Multi-Spectral Imagery (MSI) and Synthetic Aperture Radar (SAR) from publicly available satellite imagery to map and capture sub-annual...
Remote sensing, and particularly Sinthetic Aperture Radar (SAR) is a potential tool to monitor the presence of plastic on the ocean surface, and AI algorithms can learn to find patterns in the satellite signals that lead to automatic detection of plastic patches. The main challenge to train AI algorithms for this purpose is the lack of in-situ grou...
This paper proposes an unsupervised clustering algorithm for multispectral images, which automatically determines the number of statistically distinct clusters in the image. It uses the multivariate student-t distribution as a more flexible underlying statistical model, with the Gaussian as only a special case. The algorithm shows better data model...
Questions
Question (1)
For my PhD project I need to work with this type of data, collecting all possible info recorded during the acquisition step and used in the focusing stage. I have the permission from ESA to download them through Eolisa.
Unfortunately the files have extension .E1 or .E2 and and not .dat (as requested in commercial software as ENVI that reads ERS-1 Level-0 with extension dat*.001).
Is there someone who has worked with this type of data?
