Abstract
Classification of ships and icebergs in the Arctic in satellite images is an important problem. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. We also analyze synthetic-aperture radar (SAR) images for comparison. The annotated datasets of ships and icebergs are collected from multispectral Sentinel-2 data and taken from the C-CORE dataset of Sentinel-1 SAR images. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. Classification accuracies are considerably better for deep neural networks than for support vector machines. Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Extending the datasets with semi-supervised data from Greenland improves the accuracy considerably whereas data augmentation by rotating and flipping the images has little effect. The resulting classification accuracies for ships and icebergs are 86% for the SAR data and 96% for the MSI data due to the better resolution and more multispectral bands. The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.
Highlights
The Danish Royal Arctic Command monitors the ship traffic in Greenland but receives numerous false alarms from abundant icebergs
The resulting accuracies for ship and iceberg classification give the false alarm rates which are crucial for Arctic surveillance, marine situational awareness, rescue service, etc., especially for non-cooperative ships
A comparison of Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) models [19] find that the ship classification accuracy generally improves with the number of features and parameters included in the model
Summary
The Danish Royal Arctic Command monitors the ship traffic in Greenland but receives numerous false alarms from abundant icebergs. Surveillance for marine situation awareness is essential for monitoring and controlling traffic safety, piracy, smuggling, fishing, irregular migration, trespassing, spying, icebergs, shipwrecks, and the environment (oil spill or pollution), for example. “Dark ships” are non-cooperative vessels with non-functioning transponder systems such as the automatic identification system (AIS). Their transmission may be jammed, spoofed, sometimes experience erroneous returns, or turned off deliberately or by accident. AIS receivers are mostly land-based and satellite coverage is sparse at sea and high latitudes. Other non-cooperative surveillance systems as satellite or airborne systems are required for detecting ships
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