Selecting principal attributes in multimodal remote sensing for sea ice characterization
Automatic ice charting cannot be achieved using only SAR modalities. It is fundamental to combine information from other remote sensors with different characteristics for more reliable sea ice characterization. In this paper, we employ principal feature analysis (PFA) to select significant information from multimodal remote sensing data. PFA is a simple yet very effective approach that can be applied to several types of data without loss of physical interpretability. Considering that different homogeneous regions require different types of information, we perform the selection patch-wise. Accordingly, by exploiting the spatial information, we increase the robustness and accuracy of PFA.
CitationKhachatrian, Chlaily, Eltoft, Marinoni: Selecting principal attributes in multimodal remote sensing for sea ice characterization. In: VDE V. EUSAR 2021 : 13th European Conference on Synthetic Aperture Radar 29 March – 1 April 2021, Online Event, 2021. VDE Verlag GmbH p. 531-536
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