Cristin-resultat-ID: 1937726
Sist endret: 13. oktober 2021, 15:14
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

Bidragsytere:
  • Eduard Khachatrian
  • Saloua Chlaily
  • Torbjørn Eltoft
  • Wolfgang Fritz Otto Dierking
  • Frode Dinessen og
  • Andrea Marinoni

Tidsskrift

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN 1939-1404
e-ISSN 2151-1535
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Volum: 14
Sider: 9025 - 9037
Open Access

Klassifisering

Emneord

Syntetisk aperturradar • Sjøis • Fjernanalyse

Beskrivelse Beskrivelse

Tittel

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

Sammendrag

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.

Bidragsytere

Eduard Khachatrian

  • Tilknyttet:
    Forfatter
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

Saloua Chlaily

  • Tilknyttet:
    Forfatter
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

Torbjørn Eltoft

  • Tilknyttet:
    Forfatter
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

Wolfgang Fritz Otto Dierking

  • Tilknyttet:
    Forfatter
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

Frode Dinessen

  • Tilknyttet:
    Forfatter
    ved Meteorologisk institutt
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