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Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

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Title
Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
Authors
Hong, Sang-Hoon
Kim, Hyun-Ok
Wdowinski, Shimon
Feliciano, Emanuelle
Subject
Remote Sensing
Keywords
Polarimetric SAR (PolSAR); Polarimetric decomposition; TerraSAR-X; Wetland vegetation; Subtropical wetland; Everglades
Issue Date
2015
Citation
Hong ,Sang-Hoon., et al. 2015. Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types. Remote Sens, 7(7): 8563-8585.
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 wit h 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.
DOI
10.3390/rs70708563
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