Blar i forfatter "Lohse, Johannes"

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    • Analysis of time series of polarimetric sea ice signatures observed in fast ice it the Belgica Bank area 

      Eltoft, Torbjørn; Johansson, Malin; Lohse, Johannes; Ferro-Famil, Laurent (Journal article; Tidsskriftartikkel, 2023-10-20)
      The CIRFA-Cruise 2022 with <i>RV Kronprins Haakon</i> to the north-eastern coast of Greenland in the period April 22nd to May 9th 2022 was organised to perform measurements and make observations which allow for validation of sea ice remote sensing information and forecast products resulting from work in the Centre for Integrated Remote Sensing and Forecasting for Arctic Operations(CIRFA), a Centre ...

    • Cross-platform application of a sea ice classification method considering incident angle dependency of backscatter intensity and its use in separating level and deformed ice 

      Guo, Wenkai; Itkin, Polona; Lohse, Johannes; Johansson, Malin; Doulgeris, Anthony Paul (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. To perform this task with extended spatial and temporal coverage, we ...

    • Data Augmentation for SAR Sea Ice and Water Classification Based on Per-Class Backscatter Variation With Incidence Angle 

      WANG, QIANG; Lohse, Johannes; Doulgeris, Anthony Paul; Eltoft, Torbjørn (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-03)
      Monitoring sea ice in polar regions is critical for understanding global climate change and supporting marine navigation. Recently, researchers started to utilize machine/deep learning methodologies to automate the separation of sea ice and open water in synthetic aperture radar imagery. However, this requires a large amount of reliably labeled training data. We here propose an augmentation routine ...

    • Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification 

      Lohse, Johannes; Doulgeris, Anthony Paul; Dierking, Wolfgang Fritz Otto (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-04)
      Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate ...

    • Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle 

      Lohse, Johannes; Doulgeris, Anthony Paul; Dierking, Wolfgang (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-23)
      Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in ...

    • On Automated Classification of Sea Ice Types in SAR Imagery 

      Lohse, Johannes (Doctoral thesis; Doktorgradsavhandling, 2021-03-12)
      With the Arctic sea ice continuously decreasing in both extent and thickness, fast and robust production of reliable ice charts becomes more important to ensure the safety of Arctic operations. This thesis focuses on the development of automated algorithms for the mapping of sea ice from synthetic aperture radar (SAR) images. It presents a thorough background on the topics of sea ice observations ...

    • An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery 

      Lohse, Johannes; Doulgeris, Anthony Paul; Dierking, Wolfgang Fritz Otto (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-07-03)
      We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a sequence of binary problems. Each branch of the tree separates one single class from all other remaining classes, using a class-specific selected feature ...