Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images Article Swipe
Bolin Fu
,
Huajian Li
,
Man Liu
,
Hang Yao
,
Ertao Gao
,
Weiwei Sun
,
Shurong Zhang
,
Donglin Fan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecolind.2023.111246
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecolind.2023.111246
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecolind.2023.111246
- OA Status
- gold
- Cited By
- 17
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388839464
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388839464Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.ecolind.2023.111246Digital Object Identifier
- Title
-
Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-20Full publication date if available
- Authors
-
Bolin Fu, Huajian Li, Man Liu, Hang Yao, Ertao Gao, Weiwei Sun, Shurong Zhang, Donglin FanList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ecolind.2023.111246Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.ecolind.2023.111246Direct OA link when available
- Concepts
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Polarimetry, Remote sensing, Vegetation (pathology), Wetland, Marsh, Environmental science, Synthetic aperture radar, Ranging, Contextual image classification, C band, Computer science, Geology, Artificial intelligence, Image (mathematics), Scattering, Ecology, Geodesy, Physics, Biology, Optics, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 5Per-year citation counts (last 5 years)
- References (count)
-
68Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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