Deep Learning for Iceberg Detection in Satellite Images

Abstract: The application of satellite images for ship and iceberg monitoring is essential in many ways in Arctic waters. Even though the detection of ships and icebergs in images is well established using Geoscience techniques, the discrimination between those two target classes still represents a challenge for operational scenarios. This thesis project proposes the application of Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and SingleShot Detector (SSD) for ship-iceberg detection in satellite images. The CNN model is compared with SVM and SSD, and the final results indicate not only a superior classification performance of the proposed methods but also the object detection results from SSD.