Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Classification accuracies are considerably better for deep neural networks than for support vector machines. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. 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. We also analyze synthetic-aperture radar (SAR) images for comparison. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. N2 - Classification of ships and icebergs in the Arctic in satellite images is an important problem. T1 - Ship-iceberg classification in SAR and multispectral satellite images with neural networks The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.', The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. 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. 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 reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.Ībstract = 'Classification of ships and icebergs in the Arctic in satellite images is an important problem.

Classification of ships and icebergs in the Arctic in satellite images is an important problem.

Iceberg image with ship