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

Abdarahmane Traoré; Jeremy Jensen; Moulay A. Akhloufi

doi:10.1117/12.2519577

10 May 2019 Deep learning for remote sensed target classification in maritime satellite radar images

Abdarahmane Traoré, Jeremy Jensen, Moulay A. Akhloufi

Proceedings Volume 11014, Ocean Sensing and Monitoring XI; 110140E (2019) https://doi.org/10.1117/12.2519577
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Abdarahmane Traoré, Jeremy Jensen, and Moulay A. Akhloufi "Deep learning for remote sensed target classification in maritime satellite radar images", Proc. SPIE 11014, Ocean Sensing and Monitoring XI, 110140E (10 May 2019); https://doi.org/10.1117/12.2519577

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