How to cite:
Khachatrian, E.; Chlaily, S.; Eltoft, T.; Marinoni, A. Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization. Preprints2020, 2020100547. https://doi.org/10.20944/preprints202010.0547.v1
Khachatrian, E.; Chlaily, S.; Eltoft, T.; Marinoni, A. Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization. Preprints 2020, 2020100547. https://doi.org/10.20944/preprints202010.0547.v1
Khachatrian, E.; Chlaily, S.; Eltoft, T.; Marinoni, A. Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization. Preprints2020, 2020100547. https://doi.org/10.20944/preprints202010.0547.v1
APA Style
Khachatrian, E., Chlaily, S., Eltoft, T., & Marinoni, A. (2020). Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization. Preprints. https://doi.org/10.20944/preprints202010.0547.v1
Chicago/Turabian Style
Khachatrian, E., Torbjørn Eltoft and Andrea Marinoni. 2020 "Selecting Principal Attributes in Multimodal Remote Sensing for Sea Ice Characterization" Preprints. https://doi.org/10.20944/preprints202010.0547.v1
Abstract
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.
Keywords
Remote sensing; Multisensor systems; Information theory; Sea Ice
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.