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ORCIDKatalin Blix
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2020 – today
- 2023
[j8]Muhammad Asim
, Atsushi Matsuoka, Pål Gunnar Ellingsen, Camilla Brekke, Torbjørn Eltoft, Katalin Blix:
A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic). IEEE Trans. Geosci. Remote. Sens. 61: 1-19 (2023)- [c7]Huan Li, Katalin Blix, Boglárka Somogyi, Viktor R. Tóth:
Retrieving Chlorophyll-A Concentration For Lake Balaton With Landsat Based On GEE. IGARSS 2023: 460-463 - [c6]Martin H. Skjelvareid, Eli Rinde, Kasper Hancke, Katalin Blix, Galice G. Hoarau:
Mapping Marine Macroalgae along the Norwegian Coast Using Hyperspectral UAV Imaging and Convolutional Nets for Semantic Segmentation. IGARSS 2023: 4002-4005 - 2022
- [j7]Katalin Blix, Ana Belen Ruescas, Juan Emmanuel Johnson, Gustau Camps-Valls:
Learning Relevant Features of Optical Water Types. IEEE Geosci. Remote. Sens. Lett. 19: 1-5 (2022) - [j6]Marcela Pereira-Sandoval, Ana B. Ruescas, Jorge García-Jimenez, Katalin Blix, Jesús Delegido, José Moreno:
Supervised Classifications of Optical Water Types in Spanish Inland Waters. Remote. Sens. 14(21): 5568 (2022) - 2021
- [j5]Katalin Blix, Martine Mostervik Espeseth, Torbjørn Eltoft:
Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data. IEEE Trans. Geosci. Remote. Sens. 59(6): 4618-4634 (2021) - 2020
- [c5]Katalin Blix, Martine Mostervik Espeseth, Torbjørn Eltoft:
Comparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric Sar Data. IGARSS 2020: 738-741 - [c4]Katalin Blix:
Machine Learning Classification, Feature Ranking and Regression for Water Quality Parameters Retrieval in Various Optical Water Types from Hyper-Spectral Observations. IGARSS 2020: 5608-5611
2010 – 2019
- 2019
- [j4]Katalin Blix, Juan Li, Philippe Y. Massicotte, Atsushi Matsuoka:
Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI. Remote. Sens. 11(18): 2076 (2019) - [c3]Katalin Blix, Torbjørn Eltoft:
A Generalized Chlorophyll-A Estimation Model for Complexity-Diverse Arctic Waters. IGARSS 2019: 7861-7864 - 2018
- [j3]Katalin Blix, Torbjørn Eltoft:
Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval. Remote. Sens. 10(5): 775 (2018) - [j2]Katalin Blix, Torbjørn Eltoft:
Evaluation of Feature Ranking and Regression Methods for Oceanic Chlorophyll-a Estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 11(5): 1403-1418 (2018) - [c2]Katalin Blix, Martine Mostervik Espeseth, Torbjørn Eltoft:
Up-Scaling from Quad-Polarimetric to Dual-Polarimetric SAR Data Using Machine Learning Gaussian Process Regression. IGARSS 2018: 7332-7335 - 2017
- [j1]Katalin Blix, Gustau Camps-Valls, Robert Jenssen:
Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(4): 1265-1277 (2017) - 2015
- [c1]Katalin Blix, Gustau Camps-Valls, Robert Jenssen:
Sensitivity analysis of Gaussian processes for oceanic chlorophyll prediction. IGARSS 2015: 996-999
Coauthor Index
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