Comparing the behavior of polarimetric SAR imagery (TerraSAR-X and RADARSAT-2) for automated sea ice classification
Resse R, Singha S, Lehner S, Safety M (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: European Space Agency
Book Volume: SP-740
Conference Proceedings Title: European Space Agency, (Special Publication) ESA SP
Event location: Prague, CZE
ISBN: 9789292213053
Abstract
Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of singlepolarimetric classifiers. 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. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).
Involved external institutions
Germany (DE)How to cite
APA:
Resse, R., Singha, S., Lehner, S., & Safety, M. (2016). Comparing the behavior of polarimetric SAR imagery (TerraSAR-X and RADARSAT-2) for automated sea ice classification. In L. Ouwehand (Eds.), European Space Agency, (Special Publication) ESA SP. Prague, CZE: European Space Agency.
MLA:
Resse, Rudolf, et al. "Comparing the behavior of polarimetric SAR imagery (TerraSAR-X and RADARSAT-2) for automated sea ice classification." Proceedings of the Living Planet Symposium 2016, Prague, CZE Ed. L. Ouwehand, European Space Agency, 2016.
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