Statistical Analysis, Machine Learning and Image Analysis - SAMBA
The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.
Last 5 scientific articles
Liu, Qinghui; Salberg, Arnt Børre; Jenssen, Robert. A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. (ISBN 978-1-5386-7150-4). pp 6943-6946. doi: 10.1109/IGARSS.2018.8518533. 2018. Full-text
Kermit, Martin Andreas; Hamar, Jarle Bauck; Trier, Øivind Due. Towards a national infrastructure for semi-automatic mapping of cultural heritage in Norway. In: Oceans of data. Proceedings of the 44th Annual Conference on Computer Applications and Quantitative Methods in Archaeology. (ISBN 9781784917302). pp 159-172. 2018. Full-text
Salberg, Arnt Børre; Larsen, Siri Øyen. Classification of Ocean Surface Slicks in Simulated Hybrid-Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing (ISSN 0196-2892). 56(12) pp 7062-7073. doi: 10.1109/TGRS.2018.2847724. 2018. Full-text
Trier, Øivind Due; Cowley, David C.; Waldeland, Anders U.. Using deep neural networks on airborne laser scanning data: results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland. Archaeological Prospection (ISSN 1075-2196). 26(1) doi: 10.1002/arp.1731. 2018. Full-text