Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecos...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sang-Hoon Hong [verfasserIn] Hyun-Ok Kim [verfasserIn] Shimon Wdowinski [verfasserIn] Emanuelle Feliciano [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Polarimetric SAR (PolSAR) polarimetric decomposition TerraSAR-X wetland vegetation subtropical wetland Everglades

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 7(2015), 7, Seite 8563-8585
Übergeordnetes Werk:	volume:7 ; year:2015 ; number:7 ; pages:8563-8585

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs70708563

Katalog-ID:	DOAJ044508107

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spelling	10.3390/rs70708563 doi (DE-627)DOAJ044508107 (DE-599)DOAJ55284eb69fdc47389d446fc7c4822213 DE-627 ger DE-627 rakwb eng Sang-Hoon Hong verfasserin aut Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types. Polarimetric SAR (PolSAR) polarimetric decomposition TerraSAR-X wetland vegetation subtropical wetland Everglades Science Q Hyun-Ok Kim verfasserin aut Shimon Wdowinski verfasserin aut Emanuelle Feliciano verfasserin aut In Remote Sensing MDPI AG, 2009 7(2015), 7, Seite 8563-8585 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:7 year:2015 number:7 pages:8563-8585 https://doi.org/10.3390/rs70708563 kostenfrei https://doaj.org/article/55284eb69fdc47389d446fc7c4822213 kostenfrei http://www.mdpi.com/2072-4292/7/7/8563 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2015 7 8563-8585
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allfieldsSound	10.3390/rs70708563 doi (DE-627)DOAJ044508107 (DE-599)DOAJ55284eb69fdc47389d446fc7c4822213 DE-627 ger DE-627 rakwb eng Sang-Hoon Hong verfasserin aut Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types. Polarimetric SAR (PolSAR) polarimetric decomposition TerraSAR-X wetland vegetation subtropical wetland Everglades Science Q Hyun-Ok Kim verfasserin aut Shimon Wdowinski verfasserin aut Emanuelle Feliciano verfasserin aut In Remote Sensing MDPI AG, 2009 7(2015), 7, Seite 8563-8585 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:7 year:2015 number:7 pages:8563-8585 https://doi.org/10.3390/rs70708563 kostenfrei https://doaj.org/article/55284eb69fdc47389d446fc7c4822213 kostenfrei http://www.mdpi.com/2072-4292/7/7/8563 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2015 7 8563-8585
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source	In Remote Sensing 7(2015), 7, Seite 8563-8585 volume:7 year:2015 number:7 pages:8563-8585
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topic_facet	Polarimetric SAR (PolSAR) polarimetric decomposition TerraSAR-X wetland vegetation subtropical wetland Everglades Science Q
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authorswithroles_txt_mv	Sang-Hoon Hong @@aut@@ Hyun-Ok Kim @@aut@@ Shimon Wdowinski @@aut@@ Emanuelle Feliciano @@aut@@
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author	Sang-Hoon Hong
spellingShingle	Sang-Hoon Hong misc Polarimetric SAR (PolSAR) misc polarimetric decomposition misc TerraSAR-X misc wetland vegetation misc subtropical wetland misc Everglades misc Science misc Q Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
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topic_title	Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types Polarimetric SAR (PolSAR) polarimetric decomposition TerraSAR-X wetland vegetation subtropical wetland Everglades
topic	misc Polarimetric SAR (PolSAR) misc polarimetric decomposition misc TerraSAR-X misc wetland vegetation misc subtropical wetland misc Everglades misc Science misc Q
topic_unstemmed	misc Polarimetric SAR (PolSAR) misc polarimetric decomposition misc TerraSAR-X misc wetland vegetation misc subtropical wetland misc Everglades misc Science misc Q
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title	Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
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title_full	Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
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author_browse	Sang-Hoon Hong Hyun-Ok Kim Shimon Wdowinski Emanuelle Feliciano
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title_sort	evaluation of polarimetric sar decomposition for classifying wetland vegetation types
title_auth	Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
abstract	The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.
abstractGer	The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.
abstract_unstemmed	The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.
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title_short	Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
url	https://doi.org/10.3390/rs70708563 https://doaj.org/article/55284eb69fdc47389d446fc7c4822213 http://www.mdpi.com/2072-4292/7/7/8563 https://doaj.org/toc/2072-4292
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Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

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