Machine Learning based Classification of Lake ice and Open water from SAR Altimetry waveform parameters
Notice bibliographique
Résumé
Lakes cover a significant fraction of the landscape in many northern countries and play a key role in regulating weather and climate. Lakes also have a significant impact on northern communities since the presence (or absence), extent and thickness of lake ice affect transportation (ice roads), food availability, recreational activities, and tourism in wintertime. The recent decline in in-situ observations of lake ice phenology (i.e., freeze-up and break-up dates, and ice cover duration) and lake ice thickness makes remote sensing technology a viable means for monitoring lake ice conditions. Although satellite altimetry has been used in various cryospheric studies, little work has been conducted on lake ice compared to sea ice, for example. This study was conducted at Great Slave Lake, Northwest Territories, Canada, using Sentinel-3A/B SRAL Level 2 data from June 2018 to December 2020. Reflections of radar altimeter echoes differ with properties/conditions of the target and the resulting radar returns contain information about the target surface. Hence, we explored information provided by waveforms to discriminate between open water and lake ice based on machine learning. To characterize the waveforms, five waveform parameters were extracted: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient, and the maximum value of the echo power. Random Forest (RF) and Support Vector Machine (SVM) classifiers were selected to perform along-track classification of open water and lake ice. Class labelling was performed manually via visual interpretation of Sentinel-3 SRAL Level 2 waveforms, Sentinel 2 MultiSpectral Instrument (MSI) Level 1C data, and MODIS Aqua/Terra Level 1B data. Through our proposed method, we reached the highest accuracy of 91.89% (SVM) and 89.58% (RF) during the freeze-up period (November-December). Comparatively, classification performance was lower during the break-up period (late April-early June) reaching an overall accuracy of 77.19% (SVM) and 77.32% (RF). The backscatter coefficient and OCOG Width were found to be the two parameters of most importance for discriminating between ice and open water. Analysis of early results suggests that higher classification accuracies may be achieved by subdividing open water and ice into two more classes to represent leads and melting ice. In addition, pseudo LRM data from Sentinel-3 are currently being analysed and compared to results obtained with SAR data. These new results will also be presented. Keywords: SAR altimetry, lake ice, classification, waveform, machine learning
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».