Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images

作者：Bolin Fu, Huajian Li, Man Liu, Hang Yao, Ertao Gao, Weiwei Sun, Shurong Zhang, Donglin Fan · 发表于：Ecological Indicators · 年份：2023 · DOI：10.1016/j.ecolind.2023.111246 · 被引用次数：22 · 研究领域：Synthetic Aperture Radar (SAR) Applications and Techniques、Remote-Sensing Image Classification、Remote Sensing in Agriculture

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 ...