UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS
Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering...
Published in: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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ftcopernicus:oai:publications.copernicus.org:isprs-archives53062 2023-05-15T17:21:55+02:00 UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS Mohammadimanesh, F. Salehi, B. Mahdianpari, M. Homayouni, S. 2018-01-15 application/pdf https://doi.org/10.5194/isprs-archives-XLI-B7-305-2016 https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/305/2016/ eng eng doi:10.5194/isprs-archives-XLI-B7-305-2016 https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/305/2016/ eISSN: 2194-9034 Text 2018 ftcopernicus https://doi.org/10.5194/isprs-archives-XLI-B7-305-2016 2019-12-24T09:52:15Z Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data. Text Newfoundland Copernicus Publications: E-Journals Canada Newfoundland The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 305 310 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data. |
format |
Text |
author |
Mohammadimanesh, F. Salehi, B. Mahdianpari, M. Homayouni, S. |
spellingShingle |
Mohammadimanesh, F. Salehi, B. Mahdianpari, M. Homayouni, S. UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS |
author_facet |
Mohammadimanesh, F. Salehi, B. Mahdianpari, M. Homayouni, S. |
author_sort |
Mohammadimanesh, F. |
title |
UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS |
title_short |
UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS |
title_full |
UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS |
title_fullStr |
UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS |
title_full_unstemmed |
UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS |
title_sort |
unsupervised wishart classfication of wetlands in newfoundland, canada using polsar data based on fisher linear discriminant analysis |
publishDate |
2018 |
url |
https://doi.org/10.5194/isprs-archives-XLI-B7-305-2016 https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/305/2016/ |
geographic |
Canada Newfoundland |
geographic_facet |
Canada Newfoundland |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
eISSN: 2194-9034 |
op_relation |
doi:10.5194/isprs-archives-XLI-B7-305-2016 https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/305/2016/ |
op_doi |
https://doi.org/10.5194/isprs-archives-XLI-B7-305-2016 |
container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
XLI-B7 |
container_start_page |
305 |
op_container_end_page |
310 |
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1766107924931805184 |