Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time

Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time

The ability to accurately classify land cover in periods before appropriate training and validation data exist is a critical step towards understanding subtle long-term impacts of climate change. These trends cannot be properly understood and distinguished from individual disturbance events or decad...

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Hlavní autoři: Patrick Clifton Gray, Diego F. Chamorro, Justin T. Ridge, Hannah Rae Kerner, Emily A. Ury, David W. Johnston
Médium: Článek
Jazyk:English
Vydáno: MDPI AG 2021-10-01
Edice:Remote Sensing
Témata:
Landsat
land cover classification
coastal change detection
temporal generalization
deep learning
recurrent convolutional neural network
On-line přístup:https://www.mdpi.com/2072-4292/13/19/3953

Internet

https://www.mdpi.com/2072-4292/13/19/3953

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