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Proceedings Paper

Deep learning for remote sensed target classification in maritime satellite radar images
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Paper Abstract

Detecting drifting icebergs is an important task to avoid threats to navigation and offshore activities. Government and companies use aerial reconnaissance and shore-based observation platforms to detect these icebergs. However, in some areas with harsh weather conditions only satellite imagery can be used to monitor this risk. In this work, we propose the use of deep Convolutional Neural Networks to detect and classify these small remotely sensed targets as ships or icebergs. In this work, we use satellite radar imagery composed of two bands. The image patches have a resolution below 6K pixels and are noisy. To solve this challenge, we developed a deep convolutional network architecture and optimized its hyperparameters for this classification. The obtained results show that the proposed deep convolutional network achieves a very interesting accuracy for the classification of icebergs vs. ships with radar satellite images.

Paper Details

Date Published: 10 May 2019
PDF: 8 pages
Proc. SPIE 11014, Ocean Sensing and Monitoring XI, 110140E (10 May 2019); doi: 10.1117/12.2519577
Show Author Affiliations
Abdarahmane Traoré, Univ. de Moncton (Canada)
Jeremy Jensen, Univ. de Moncton (Canada)
Moulay A. Akhloufi, Univ. de Moncton (Canada)

Published in SPIE Proceedings Vol. 11014:
Ocean Sensing and Monitoring XI
Weilin "Will" Hou; Robert A. Arnone, Editor(s)

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