Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset

Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset

Land cover mapping from satellite images has progressed from visual and statistical approaches to Random Forests (RFs) and, more recently, advanced image recognition techniques such as convolutional neural networks (CNNs). CNNs have a conceptual benefit over RFs in recognising spatial feature contex...

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Bibliographic Details
Main Authors: Tony Boston, Albert Van Dijk, Pablo Rozas Larraondo, Richard Thackway
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Remote Sensing
Subjects:
land cover
Landsat data
segmentation
convolutional neural networks
random forests
deep learning
Online Access:https://www.mdpi.com/2072-4292/14/14/3396

Internet

https://www.mdpi.com/2072-4292/14/14/3396

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