Blar i forfatter "Blix, Katalin"

Viser treff 1-15 av 15

    • Comparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric SAR Data 

      Blix, Katalin; Espeseth, Martine; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-17)
      This paper addresses the problem of up-scaling full polarimetric (quad-pol) parameters from small quad-pol synthetic aperture radar (SAR) scenes to large dual-pol scenes, using a sophisticated Machine Learning (ML) method, namely the Gaussian Process Regression (GPR). The approach is to let the GPR model learn the relationships between the dual-pol input data and the quad-pol parameters on a quad-pol ...

    • 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 

      Blix, Katalin; Li, Juan; Massicotte, Philippe; Matsuoka, Atsushi (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-09-04)
      The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B ...

    • Earlier sea-ice melt extends the oligotrophic summer period in the Barents Sea with low algal biomass and associated low vertical flux 

      Kohlbach, Doreen; Goraguer, Lucie; Bodur, Yasemin V.; Müller, Oliver; Amargant Arumí, Martí; Blix, Katalin; Bratbak, Gunnar; Chierici, Melissa; Dabrowska, Anna Maria; Dietrich, Ulrike; Edvardsen, Bente; Garcia, Laura; Gradinger, Rolf Rudolf; Hop, Haakon; Jones, Elizabeth Marie; Øyvind, Lundesgaard; Olsen, Lasse Mork; Reigstad, Marit; Saubrekka, Karoline; Tatarek, Agnieszka; Wiktor, Josef Maria; Wold, Anette; Assmy, Philipp (Journal article; Tidsskriftartikkel, 2023-03-27)
      The decrease in Arctic sea-ice extent and thickness as a result of global warming will impact the timing, duration, magnitude and composition of phytoplankton production with cascading effects on Arctic marine food-webs and biogeochemical cycles. Here, we elucidate the environmental drivers shaping the composition, abundance, biomass, trophic state and vertical flux of protists (unicellular eukaryotes), ...

    • Evaluation of feature ranking and regression methods for oceanic chlorophyll-a estimation 

      Blix, Katalin; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-03-22)
      This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second ...

    • Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation 

      Blix, Katalin; Camps-Valls, Gustau; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-01-04)
      Gaussian process regression (GPR) has experienced tremendous success in biophysical parameter retrieval in the past years. The GPR provides a full posterior predictive distribution so one can derive mean and variance predictive estimates, i.e., point-wise predictions and associated confidence intervals. GPR typically uses translation invariant covariances that make the prediction function very ...

    • The law of the sea and current practices of marine scientific research in the Arctic 

      Woker, Hilde; Schartmüller, Bernhard; Dølven, Knut Ola; Blix, Katalin (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-02-07)
      The rapid changes in both climate and human activity occurring in the Arctic Ocean demands improved knowledge about this region. Combined with eased accessibility due to reduced sea ice cover and new technologies, this has led to increased research activity in the region. These circumstances put pressure on the applicable legal framework, i.e. the United Nations Convention on the Law of the Sea. ...

    • Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval 

      Blix, Katalin; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-05-17)
      Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely ...

    • Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data 

      Blix, Katalin; Espeseth, Martine; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-22)
      This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and ...

    • Machine Learning simulations of quad-polarimetric features from dual-polarimetric measurements over sea ice 

      Blix, Katalin; Espeseth, Martine; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-06)
      In this paper, we investigated the capabilities of the Gaussian Process Regression (GPR) algorithm in predicting of two quad-polarimetric parameters (relevant for sea ice analysis) from 6-dimensional dual-polarimetric input vectors. The GRP is trained on few hundred samples selected randomly from an image subset, and tested on the entire image. The performance is assessed by visual comparisons, and ...

    • Machine Learning Water Quality Monitoring 

      Blix, Katalin (Doctoral thesis; Doktorgradsavhandling, 2019-09-13)
      This work utilizes Machine Learning (ML) regression and feature ranking techniques for water quality monitoring from remotely sensed data. The investigated regression methods include the Gaussian Process Regression (GPR), Suport Vector Regression (SVR) and Partial Least Squares Regression (PLSR). Feature relevance in the GPR model is as- sessed by the probabilistic Sensitivity Analysis (SA) approach.This ...

    • Mapping Marine Macroalgae along the Norwegian Coast Using Hyperspectral UAV Imaging and Convolutional Nets for Semantic Segmentation 

      Skjelvareid, Martin Hansen; Rinde, Eli; Hancke, Kasper; Blix, Katalin; Hoarau, Galice Guillaume (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-20)
      Marine macroalgae form underwater "blue forests" with several important functions. Hyperspectral imaging from unmanned aerial vehicles provides a rich set of spectral and spatial data that can be used to map the distribution of such macroalgae. Results from a study using 81 annotated hyper-spectral images from the Norwegian coast are presented. A U-net convolutional network was used for classification, ...

    • 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) 

      Asim, Muhammad; Matsuoka, Atsushi; Ellingsen, Pål Gunnar; Brekke, Camilla; Eltoft, Torbjørn; Blix, Katalin (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-12)
      The synergistic use of Landsat-8 operational land imager (OLI) and Sentinel-2 multispectral instrument (MSI) data products provides an excellent opportunity to monitor the dynamics of aquatic ecosystems. However, the merging of data products from multisensors is often adversely affected by the difference in their spectral characteristics. In addition, the errors in the atmospheric correction (AC) ...

    • Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI 

      Blix, Katalin; Pálffy, Károly; Tóth, Viktor R.; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-10-11)
      The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been ...

    • Sensitivity analysis of Gaussian process machine learning for chlorophyll prediction from optical remote sensing 

      Blix, Katalin (Master thesis; Mastergradsoppgave, 2014-05-30)
      The machine learning method, Gaussian Process Regression (GPR), has lately been introduced for chlorophyll content mapping from remotely sensed data. It has been shown that GPR has outperformed other machine learning and empirical methods in accuracy, speed and stability. Moreover, GPR not only estimates the chlorophyll content, it also provides the certainty level of the prediction, allowing the ...

    • Supervised Classifications of Optical Water Types in Spanish Inland Waters 

      Pereira-Sandoval, Marcela; Ruescas, Ana Belen; García-Jimenez, Jorge; Blix, Katalin; Delegido, Jesús; Moreno, José (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-04)
      Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in ...