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Communication Dans Un Congrès Année : 2023

CNN Classification of Wet Snow By Physical Snowpack Model Labeling

Résumé

We propose a new approach for wet snow extent mapping in Synthetic Aperture Radar (SAR) images by using a convolutional neural network (CNN) designed to learn with respect to snowpack outputs from the state-of-the-art snow model Crocus. The CNN was trained to classify the wet snow conditions based on features extracted from the SAR images, using both the VV,VH channel and the ratio between these channels and those of a reference image in summer. One of the key points of this work is the comprehensive comparison we have made between the performance of the CNN method and other advanced statistical methods.We found that the CNN was able to achieve good accuracy in wet snow classification, and giving a complementary vision of the solutions obtained by other machine learning algorithms such as the Random Forest classifier. The results of this study demonstrate the potential of using CNNs and SAR images for wet snow classification and highlight the importance of using physical information model for training machine learning models in snow state identification, a domain where collecting ground truth is intricate due to the complexity of the snowpack moisture measurement systems.
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Dates et versions

hal-04171432 , version 1 (26-07-2023)

Identifiants

  • HAL Id : hal-04171432 , version 1

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Matthieu Gallet, Abdourrahmane Atto, Emmanuel Trouvé, Fatima Karbou. CNN Classification of Wet Snow By Physical Snowpack Model Labeling. International Geoscience and Remote Sensing Symposium (IGARSS 2023), Jul 2023, Pasadena, CA, United States. ⟨hal-04171432⟩
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