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Remote Sens. 2016, 8(3), 198; doi:10.3390/rs8030198

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Maritime Security Research Center, Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Henrich Focke Str. 4, 28199 Bremen, Germany
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editors: Walt Meier, Mark Tschudi, Xiaofeng Li and Prasad S. Thenkabail
Received: 3 November 2015 / Revised: 29 January 2016 / Accepted: 24 February 2016 / Published: 29 February 2016
(This article belongs to the Special Issue Sea Ice Remote Sensing and Analysis)

Abstract

This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types. View Full-Text
Keywords: polarimetry; sea ice; feature evaluation; artificial neural network polarimetry; sea ice; feature evaluation; artificial neural network
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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. (CC BY 4.0).

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MDPI and ACS Style

Ressel, R.; Singha, S. Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification. Remote Sens. 2016, 8, 198.

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