The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method
Speckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image,...
Published in: | Canadian Journal of Remote Sensing |
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Taylor & Francis Group
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ftdoajarticles:oai:doaj.org/article:4074994b3c264517bd692eb488b13497 2023-11-12T04:21:22+01:00 The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh 2017-09-01T00:00:00Z https://doi.org/10.1080/07038992.2017.1381549 https://doaj.org/article/4074994b3c264517bd692eb488b13497 EN FR eng fre Taylor & Francis Group http://dx.doi.org/10.1080/07038992.2017.1381549 https://doaj.org/toc/1712-7971 1712-7971 doi:10.1080/07038992.2017.1381549 https://doaj.org/article/4074994b3c264517bd692eb488b13497 Canadian Journal of Remote Sensing, Vol 43, Iss 5, Pp 485-503 (2017) Environmental sciences GE1-350 Technology T article 2017 ftdoajarticles https://doi.org/10.1080/07038992.2017.1381549 2023-10-15T00:36:32Z Speckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy. Article in Journal/Newspaper Newfoundland Directory of Open Access Journals: DOAJ Articles Canada Canadian Journal of Remote Sensing 43 5 485 503 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English French |
topic |
Environmental sciences GE1-350 Technology T |
spellingShingle |
Environmental sciences GE1-350 Technology T Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method |
topic_facet |
Environmental sciences GE1-350 Technology T |
description |
Speckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy. |
format |
Article in Journal/Newspaper |
author |
Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh |
author_facet |
Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh |
author_sort |
Masoud Mahdianpari |
title |
The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method |
title_short |
The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method |
title_full |
The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method |
title_fullStr |
The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method |
title_full_unstemmed |
The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method |
title_sort |
effect of polsar image de-speckling on wetland classification: introducing a new adaptive method |
publisher |
Taylor & Francis Group |
publishDate |
2017 |
url |
https://doi.org/10.1080/07038992.2017.1381549 https://doaj.org/article/4074994b3c264517bd692eb488b13497 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Canadian Journal of Remote Sensing, Vol 43, Iss 5, Pp 485-503 (2017) |
op_relation |
http://dx.doi.org/10.1080/07038992.2017.1381549 https://doaj.org/toc/1712-7971 1712-7971 doi:10.1080/07038992.2017.1381549 https://doaj.org/article/4074994b3c264517bd692eb488b13497 |
op_doi |
https://doi.org/10.1080/07038992.2017.1381549 |
container_title |
Canadian Journal of Remote Sensing |
container_volume |
43 |
container_issue |
5 |
container_start_page |
485 |
op_container_end_page |
503 |
_version_ |
1782336815073591296 |