Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar
Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring 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 extrac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2016-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7529171/ |
| _version_ | 1818647823688663040 |
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| author | Rudolf Ressel Suman Singha Susanne Lehner Anja Rosel Gunnar Spreen |
| author_facet | Rudolf Ressel Suman Singha Susanne Lehner Anja Rosel Gunnar Spreen |
| author_sort | Rudolf Ressel |
| collection | DOAJ |
| description | Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring 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 be more 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. |
| first_indexed | 2024-12-17T01:08:40Z |
| format | Article |
| id | doaj.art-3084bfc6b1ed438abbb98f9bca5a9938 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-17T01:08:40Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-3084bfc6b1ed438abbb98f9bca5a99382022-12-21T22:09:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352016-01-01973131314310.1109/JSTARS.2016.25395017529171Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture RadarRudolf Ressel0https://orcid.org/0000-0002-1880-6868Suman Singha1Susanne Lehner2Anja Rosel3Gunnar Spreen4Maritime Safety and Security Lab, Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Bremen, GermanyMaritime Safety and Security Lab, Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Bremen, GermanyMaritime Safety and Security Lab, Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Bremen, GermanyNorwegian Polar Institute (NPI), Tromsø, NorwayNorwegian Polar Institute (NPI), Tromsø, NorwaySatellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring 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 be more 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.https://ieeexplore.ieee.org/document/7529171/Artificial neural network (ANN)feature evaluationpolarimetrysea ice classificationTerraSAR-X |
| spellingShingle | Rudolf Ressel Suman Singha Susanne Lehner Anja Rosel Gunnar Spreen 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 Artificial neural network (ANN) feature evaluation polarimetry sea ice classification TerraSAR-X |
| title | Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar |
| title_full | Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar |
| title_fullStr | Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar |
| title_full_unstemmed | Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar |
| title_short | Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar |
| title_sort | investigation into different polarimetric features for sea ice classification using x band synthetic aperture radar |
| topic | Artificial neural network (ANN) feature evaluation polarimetry sea ice classification TerraSAR-X |
| url | https://ieeexplore.ieee.org/document/7529171/ |
| work_keys_str_mv | AT rudolfressel investigationintodifferentpolarimetricfeaturesforseaiceclassificationusingxbandsyntheticapertureradar AT sumansingha investigationintodifferentpolarimetricfeaturesforseaiceclassificationusingxbandsyntheticapertureradar AT susannelehner investigationintodifferentpolarimetricfeaturesforseaiceclassificationusingxbandsyntheticapertureradar AT anjarosel investigationintodifferentpolarimetricfeaturesforseaiceclassificationusingxbandsyntheticapertureradar AT gunnarspreen investigationintodifferentpolarimetricfeaturesforseaiceclassificationusingxbandsyntheticapertureradar |