Automatic Maritime Object Detection Using Satellite imagery
Konstantina Bereta, Raffaele Grasso & Dimitris Zissis
IEEE Oceans 2020, 2020
Abstract
In this paper we present an approach for performing object classification and segmentation in satellite images for the Maritime domain. We employ neural network architectures for object classification and segmentation tasks in order to identify different classes of objects in satellite imagery for the maritime domain, such as vessels, land (e.g., port terminals), clouds, etc. We compare the accuracy of different neural network architectures and present the results of our experimental evaluation.
This work was partially funded by the EU project INFORE and by NATO ACT project Data Knowledge & Operational Effectiveness.
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