Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization

Eduard Khachatrian; Saloua Chlaily; Torbjørn Eltoft; Andrea Marinoni

doi:10.20944/preprints202010.0547.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 26 October 2020 / Approved: 27 October 2020 / Online: 27 October 2020 (11:27:40 CET)

(This article belongs to the Research Topic EUSAR 2020—Preprints)

Automatic ice charting can not 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.

Remote sensing; Multisensor systems; Information theory; Sea Ice

Computer Science and Mathematics, Algebra and Number Theory

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Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization

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