MACHINE LEARNING ALGORITHMS ASSESSMENT FOR SNOW LWC RETRIEVAL FROM SAR DATA

被引：0
|
作者：
Santi, Emanuele [1 ]
Paloscia, Simonetta [1 ]
Pettinato, Simone [1 ]
Baroni, Fabrizio [1 ]
Pilia, S. [1 ]
Colombo, Roberto [2 ]
Ravasio, Claudia [2 ]
Di Mauro, Biagio [3 ]
机构：
[1] CNR, Inst Appl Phys CNR IFAC, Florence, Italy
[2] Univ Milano Bicocca, Dept Earth & Environm Sci, Milan, Italy
[3] ISP CNR, Inst Polar Sci, Venice, Italy
来源：
IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024 | 2024年
关键词：
SAR; snow; machine learning algorithms; Artificial Neural Network; Random Forest; MODEL;
D O I：
10.1109/IGARSS53475.2024.10642380
中图分类号：
P9 [自然地理学];
学科分类号：
0705 ; 070501 ;
摘要：
In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X-band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as "model-driven". The second one was defined as "data-driven". The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" R-ANN = 0.60, R-RF = 0.68; "data-driven" R-ANN = 0.50, R-RF = 0.88 at X-band). Moreover, the RF implementation trained with the "data-driven" approach outperformed the "model-driven" approach in terms of correlation coefficient R (R-RF = 0.88 at X-band).
引用
收藏
页码：1657 / 1660
页数：4
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