| Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks M Kampffmeyer, AB Salberg, R Jenssen Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 728 | 2016 |
| Machine intelligence and the data-driven future of marine science K Malde, NO Handegard, L Eikvil, AB Salberg ICES Journal of Marine Science 77 (4), 1274-1285, 2020 | 170 | 2020 |
| Separation of Norwegian coastal cod and Northeast Arctic cod by outer otolith shape analysis C Stransky, H Baumann, SE Fevolden, A Harbitz, H Høie, KH Nedreaas, ... Fisheries Research 90 (1-3), 26-35, 2008 | 153 | 2008 |
| Oil spill detection in hybrid-polarimetric SAR images AB Salberg, Ø Rudjord, AHS Solberg IEEE Transactions on Geoscience and Remote Sensing 52 (10), 6521-6533, 2014 | 147 | 2014 |
| Dense dilated convolutions’ merging network for land cover classification Q Liu, M Kampffmeyer, R Jenssen, AB Salberg IEEE Transactions on Geoscience and Remote Sensing 58 (9), 6309-6320, 2020 | 135 | 2020 |
| Doppler and frequency-offset synchronization in wideband OFDM AB Salberg, A Swami IEEE Transactions on Wireless Communications 4 (6), 2870-2881, 2005 | 91 | 2005 |
| Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data ØD Trier, AB Salberg, M Kermit, Ø Rudjord, T Gobakken, E Næsset, ... European Journal of Remote Sensing 51 (1), 336-351, 2018 | 84 | 2018 |
| Deep divergence-based approach to clustering M Kampffmeyer, S Løkse, FM Bianchi, L Livi, AB Salberg, R Jenssen Neural Networks 113, 91-101, 2019 | 76 | 2019 |
| Detection of seals in remote sensing images using features extracted from deep convolutional neural networks AB Salberg 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2015 | 70 | 2015 |
| Accuracy and precision in stock separation of north-east Arctic and Norwegian coastal cod by otoliths–comparing readings, image analyses and a genetic method E Berg, TH Sarvas, A Harbitz, SE Fevolden, AB Salberg Marine and Freshwater Research 56 (5), 753-762, 2005 | 67 | 2005 |
| Acoustic classification in multifrequency echosounder data using deep convolutional neural networks O Brautaset, AU Waldeland, E Johnsen, K Malde, L Eikvil, AB Salberg, ... ICES Journal of Marine Science 77 (4), 1391-1400, 2020 | 63 | 2020 |
| Urban land cover classification with missing data modalities using deep convolutional neural networks M Kampffmeyer, AB Salberg, R Jenssen IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2018 | 59 | 2018 |
| Semi-automatic mapping of charcoal kilns from airborne laser scanning data using deep learning ØD Trier, AB Salberg, LH Pilø CAA2016: Oceans of Data. Proceedings of the 44th Conference on Computer …, 2018 | 52 | 2018 |
| Multi-view self-constructing graph convolutional networks with adaptive class weighting loss for semantic segmentation Q Liu, MC Kampffmeyer, R Jenssen, AB Salberg Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 49 | 2020 |
| Land cover classification of cloud-contaminated multitemporal high-resolution images AB Salberg IEEE Transactions on Geoscience and Remote Sensing 49 (1), 377-387, 2010 | 43 | 2010 |
| Towards accurate age determination of Greenland halibut OT Albert, M Kvalsund, T Vollen, AB Salberg Journal of Northwest Atlantic Fishery Science 40, 2008 | 37 | 2008 |
| The 1st agriculture-vision challenge: Methods and results MT Chiu, X Xu, K Wang, J Hobbs, N Hovakimyan, TS Huang, H Shi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 34 | 2020 |
| Explaining decisions of deep neural networks used for fish age prediction A Ordoñez, L Eikvil, AB Salberg, A Harbitz, SM Murray, MC Kampffmeyer PloS one 15 (6), e0235013, 2020 | 33 | 2020 |
| Forest mapping and monitoring in Africa using Sentinel-2 data and deep learning AU Waldeland, ØD Trier, AB Salberg International Journal of Applied Earth Observation and Geoinformation 111 …, 2022 | 32 | 2022 |
| Ultrasonic imaging apparatus for reading and decoding machine-readable matrix symbols F Melandso, T Baarstad, AB Salberg, T Melandso, E Skoglund US Patent 8,662,395, 2014 | 31 | 2014 |
