Automated Ice-Water Classification using Dual Polarization SAR Imagery
Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current proces...
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University of Waterloo
2013
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ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/7706 2023-05-15T15:40:37+02:00 Automated Ice-Water Classification using Dual Polarization SAR Imagery Leigh, Steve 2013 http://hdl.handle.net/10012/7706 en eng University of Waterloo http://hdl.handle.net/10012/7706 SAR synthetic aperture radar IRGS SVM support vector machine classification sea ice RADARSAT-2 grey-level co-occurrence matrix GLCM System Design Engineering Master Thesis 2013 ftunivwaterloo 2022-06-18T22:59:45Z Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use. Master Thesis Beaufort Sea Sea ice University of Waterloo, Canada: Institutional Repository |
institution |
Open Polar |
collection |
University of Waterloo, Canada: Institutional Repository |
op_collection_id |
ftunivwaterloo |
language |
English |
topic |
SAR synthetic aperture radar IRGS SVM support vector machine classification sea ice RADARSAT-2 grey-level co-occurrence matrix GLCM System Design Engineering |
spellingShingle |
SAR synthetic aperture radar IRGS SVM support vector machine classification sea ice RADARSAT-2 grey-level co-occurrence matrix GLCM System Design Engineering Leigh, Steve Automated Ice-Water Classification using Dual Polarization SAR Imagery |
topic_facet |
SAR synthetic aperture radar IRGS SVM support vector machine classification sea ice RADARSAT-2 grey-level co-occurrence matrix GLCM System Design Engineering |
description |
Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use. |
format |
Master Thesis |
author |
Leigh, Steve |
author_facet |
Leigh, Steve |
author_sort |
Leigh, Steve |
title |
Automated Ice-Water Classification using Dual Polarization SAR Imagery |
title_short |
Automated Ice-Water Classification using Dual Polarization SAR Imagery |
title_full |
Automated Ice-Water Classification using Dual Polarization SAR Imagery |
title_fullStr |
Automated Ice-Water Classification using Dual Polarization SAR Imagery |
title_full_unstemmed |
Automated Ice-Water Classification using Dual Polarization SAR Imagery |
title_sort |
automated ice-water classification using dual polarization sar imagery |
publisher |
University of Waterloo |
publishDate |
2013 |
url |
http://hdl.handle.net/10012/7706 |
genre |
Beaufort Sea Sea ice |
genre_facet |
Beaufort Sea Sea ice |
op_relation |
http://hdl.handle.net/10012/7706 |
_version_ |
1766373271912054784 |