Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier-Feature Assembly

Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2017-11, Vol.14 (11), p.1948-1952
Hauptverfasser:	Shen, Xiaoyi, Zhang, Jie, Zhang, Xi, Meng, Junmin, Ke, Changqing
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Altimeter waveform Altimeters Arctic Arctic ice Artificial neural networks Assembly Back propagation networks Bayesian analysis Classification Classifiers Climate prediction Cryosat-2 (CS-2) Data Detection Evaluation Feature extraction Learning algorithms Machine learning Methods Navigation Nearest-neighbor Neural networks Polar environments Probability theory Radio frequency Sea ice sea ice type Sea ice types Sea level Sea surface Support vector machines Width
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