Working Paper Article Version 1 This version is not peer-reviewed

Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning

Version 1 : Received: 6 October 2021 / Approved: 7 October 2021 / Online: 7 October 2021 (23:04:24 CEST)

A peer-reviewed article of this Preprint also exists.

Ciocarlan, A.; Stoian, A. Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning. Remote Sens. 2021, 13, 4255. Ciocarlan, A.; Stoian, A. Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning. Remote Sens. 2021, 13, 4255.

Journal reference: Remote Sens. 2021, 13, 4255
DOI: 10.3390/rs13214255

Abstract

Automatic ship detection provides an essential function towards maritime domain awareness for security or economic monitoring purposes. This work presents an approach for training a deep learning ship detector in Sentinel-2 multispectral images with few labeled examples. We design a network architecture for detecting ships with a backbone that can be pre-trained separately. By using Self Supervised Learning, an emerging unsupervised training procedure, we learn good features on Sentinel-2 images, without requiring labeling, to initialize our network’s backbone. The full network is then fine-tuned to learn to detect ships in challenging settings. We evaluated this approach versus pre-training on ImageNet and versus a classical image processing pipeline. We examined the impact of variations in the self-supervised learning step and we show that in the few-shot learning setting self-supervised pre-training achieves better results than ImageNet pre-training. When enough training data is available, our self-supervised approach is as good as ImageNet pre-training. We conclude that a better design of the self-supervised task and bigger non-annotated dataset sizes can lead to surpassing ImageNet pre-training performance without any annotation costs.

Keywords

Ship detection; self-supervised learning; transfer learning; Sentinel 2

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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