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

页码：1657 / 1660

页数：4

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