Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images
Wetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wet...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23013882 |
| _version_ | 1827609598624792576 |
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| author | Bolin Fu Huajian Li Man Liu Hang Yao Ertao Gao Weiwei Sun Shurong Zhang Donglin Fan |
| author_facet | Bolin Fu Huajian Li Man Liu Hang Yao Ertao Gao Weiwei Sun Shurong Zhang Donglin Fan |
| author_sort | Bolin Fu |
| collection | DOAJ |
| description | Wetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wetland ecosystems. This study addressed to explore classification performance of backscattering coefficients and polarimetric decomposition parameters of multi-frequency SAR images, including single-polarimetric X-band TerraSAR (TS), full-polarimetric C-band Radarsat-2 (RS) and L-band ALOS PALSAR-2 (PS) for marsh vegetation mapping in Honghe National Nature Reserve, Northeast China. We proposed two transfer-learning strategies, and examined the feasibility of transfer-learning vegetation classifications between optical and SAR sensors, different frequencies SAR (X-, C- and L-band) and its derivative images, respectively. This paper further compared transfer-learning classification performance of marsh vegetation from polarimetric decomposition parameter images to backscattering coefficient images under the same SAR sensors. The results indicated that: (1) The three SAR images performed good classification ability in identifying marsh vegetation with the overall accuracies (OA) ranging from 0.74 to 0.88. For the same frequency SAR images, polarimetric decomposition parameters outperformed backscattering coefficients with an OA improvement ranging from 0.24 % to 2.41 %; (2) The longer wavelength SAR images produced better classification results, and L-band PS images realized the highest classification accuracy (OA = 0.871); (3) Full-polarimetric SAR images obtained the better transfer-learning classifications (OA > 0.8), and the transfer-learning classification ability of the same frequency SAR images outperformed the different frequencies; (4) The transfer learning classification results between SAR sensors outperformed that of between optical and SAR images. The results of this study provide a scientific basis for wetland change monitoring conservation and sustainable development. |
| first_indexed | 2024-03-09T07:35:45Z |
| format | Article |
| id | doaj.art-def8a802c0724a3f9ef6a77e076b1abe |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-09T07:35:45Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-def8a802c0724a3f9ef6a77e076b1abe2023-12-03T05:40:06ZengElsevierEcological Indicators1470-160X2023-12-01157111246Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR imagesBolin Fu0Huajian Li1Man Liu2Hang Yao3Ertao Gao4Weiwei Sun5Shurong Zhang6Donglin Fan7College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 5430079, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China; Corresponding author.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaWetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wetland ecosystems. This study addressed to explore classification performance of backscattering coefficients and polarimetric decomposition parameters of multi-frequency SAR images, including single-polarimetric X-band TerraSAR (TS), full-polarimetric C-band Radarsat-2 (RS) and L-band ALOS PALSAR-2 (PS) for marsh vegetation mapping in Honghe National Nature Reserve, Northeast China. We proposed two transfer-learning strategies, and examined the feasibility of transfer-learning vegetation classifications between optical and SAR sensors, different frequencies SAR (X-, C- and L-band) and its derivative images, respectively. This paper further compared transfer-learning classification performance of marsh vegetation from polarimetric decomposition parameter images to backscattering coefficient images under the same SAR sensors. The results indicated that: (1) The three SAR images performed good classification ability in identifying marsh vegetation with the overall accuracies (OA) ranging from 0.74 to 0.88. For the same frequency SAR images, polarimetric decomposition parameters outperformed backscattering coefficients with an OA improvement ranging from 0.24 % to 2.41 %; (2) The longer wavelength SAR images produced better classification results, and L-band PS images realized the highest classification accuracy (OA = 0.871); (3) Full-polarimetric SAR images obtained the better transfer-learning classifications (OA > 0.8), and the transfer-learning classification ability of the same frequency SAR images outperformed the different frequencies; (4) The transfer learning classification results between SAR sensors outperformed that of between optical and SAR images. The results of this study provide a scientific basis for wetland change monitoring conservation and sustainable development.http://www.sciencedirect.com/science/article/pii/S1470160X23013882Marsh vegetation classificationMulti-frequency SAR imagesBackscattering coefficientPolarimetric decomposition parameterTransfer learning |
| spellingShingle | Bolin Fu Huajian Li Man Liu Hang Yao Ertao Gao Weiwei Sun Shurong Zhang Donglin Fan Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images Ecological Indicators Marsh vegetation classification Multi-frequency SAR images Backscattering coefficient Polarimetric decomposition parameter Transfer learning |
| title | Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images |
| title_full | Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images |
| title_fullStr | Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images |
| title_full_unstemmed | Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images |
| title_short | Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images |
| title_sort | performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi sensor and multi frequency sar images |
| topic | Marsh vegetation classification Multi-frequency SAR images Backscattering coefficient Polarimetric decomposition parameter Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23013882 |
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