Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud c...

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Bibliographic Details
Main Authors: Zachary L. Langford, Jitendra Kumar, Forrest M. Hoffman, Amy L. Breen, Colleen M. Iversen
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Remote Sensing
Subjects:
hyperspectral
field-scale mapping
arctic
vegetation classification
convolutional neural network
Online Access:http://www.mdpi.com/2072-4292/11/1/69

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