Ressel, Rudolf and Singha, Suman and Lehner, Susanne and Rösel, Anja and Spreen, Gunnar (2016) Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (7), pp. 3131-3143. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2016.2539501. ISSN 1939-1404.
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Official URL: http://dx.doi.org/10.1109/JSTARS.2016.2539501
Abstract
Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea icemonitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH–VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to bemore useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.
Item URL in elib: | https://elib.dlr.de/98219/ | ||||||||||||||||||
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Document Type: | Article | ||||||||||||||||||
Additional Information: | Special Issue on “GeoVision: Computer Vision for Geospatial Applications”; PDF with open access | ||||||||||||||||||
Title: | Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar | ||||||||||||||||||
Authors: |
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Date: | 2 August 2016 | ||||||||||||||||||
Journal or Publication Title: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | ||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||
In ISI Web of Science: | Yes | ||||||||||||||||||
Volume: | 9 | ||||||||||||||||||
DOI : | 10.1109/JSTARS.2016.2539501 | ||||||||||||||||||
Page Range: | pp. 3131-3143 | ||||||||||||||||||
Publisher: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||
Series Name: | Special Issue on “GeoVision: Computer Vision for Geospatial Applications” | ||||||||||||||||||
ISSN: | 1939-1404 | ||||||||||||||||||
Status: | Published | ||||||||||||||||||
Keywords: | Sea ice classification, polarimetry, TerraSAR-X, Artificial Neural Network, Feature Evaluation | ||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||
HGF - Program Themes: | Earth Observation | ||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||
DLR - Program: | R EO - Earth Observation | ||||||||||||||||||
DLR - Research theme (Project): | R - Vorhaben Entwicklung und Erprobung von Verfahren zur Gewässerfernerkundung (old) | ||||||||||||||||||
Location: | Bremen , Oberpfaffenhofen | ||||||||||||||||||
Institutes and Institutions: | Remote Sensing Technology Institute > SAR Signal Processing Remote Sensing Technology Institute | ||||||||||||||||||
Deposited By: | Kaps, Ruth | ||||||||||||||||||
Deposited On: | 22 Jan 2016 08:59 | ||||||||||||||||||
Last Modified: | 19 Nov 2021 20:28 |
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