Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series

作者：Bolin Fu, Yiyin Liang, Zhinan Lao, Xidong Sun, Sunzhe Li, Hongchang He, Weiwei Sun, Donglin Fan · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2023 · DOI：10.1016/j.jag.2023.103446 · 被引用次数：85 · 研究领域：Coastal wetland ecosystem dynamics、Remote-Sensing Image Classification、Remote Sensing and LiDAR Applications

Mangroves play a significant role in carbon sequestration and storage. Mapping mangrove species and monitoring their conditions have been a crucial issue for achieving sustainable development goals. Currently combing multidimensional optical and SAR images with machine learning have become an important approach for mangrove species classification, but there are still some challenges in feature selection and hyperparameter optimizations. In this study, we proposed a novel classification framework by combing multi-scale variable selection algorithm (MUVR) with state-of-the-art machine learning hyperparameter optimization method (Optuna) for mapping mangrove species in the Beilun Estuary and Maowei Sea nature reserves using optical and dual-polarization SAR images, and further quantified the scattering characteristics of mangrove species using SAR image time series. We found that: (1) The MUVR algorithm could determine the optimal scale features for different scenarios and mangrove species, and improve the classification performance of machine learning with an overall accuracy (OA) improvement of 12.85%; (2) The Optuna-based optimal CatBoost outperforms LightGBM and NGBoost algorithms in mapping mangrove species, which achieved the highest OA (93.18%). This study demonstrated that LightGBM was suitable for identifying Aegiceras corniculatum, while the CatBoost algorithm was suitable for discriminating Avicennia marina, Bruguiera gymnorrhiza, Cyperus malaccensis, Kandelia candel ...