Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images

In this paper, we describe an innovative content annotation method for high-resolution Synthetic Aperture Radar (SAR) images generating routinely user-defined semantic labels for sequences of small contiguous image patches, while the full surface areas of our images cover hundreds of km in width and...

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Bibliographic Details
Published in:2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Main Authors: Dumitru, Corneliu Octavian, Schwarz, Gottfried, Karmakar, Chandrabali, Datcu, Mihai
Format: Conference Object
Language:unknown
Published: 2021
Subjects:
EO Data Science
Sea ice
Online Access:https://elib.dlr.de/142804/
https://igarss2021.com/view_paper.php?PaperNum=3147
Description
Summary:In this paper, we describe an innovative content annotation method for high-resolution Synthetic Aperture Radar (SAR) images generating routinely user-defined semantic labels for sequences of small contiguous image patches, while the full surface areas of our images cover hundreds of km in width and length. Based on this method, we are able to generate a sea-ice dataset that is used in projects to validate the developed machine learning methods.

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