Very High Resolution Remote Sensing Imagery Classification Using a Fusion of Random Forest and Deep Learning Technique—Subtropical Area for Example

Very High Resolution Remote Sensing Imagery Classification Using a Fusion of Random Forest and Deep Learning Technique—Subtropical Area for Example

Recently, convolutional neural networks (CNNs) showed excellent performance in many tasks, such as computer vision and remote sensing semantic segmentation. Especially, the ability to learn high-representation features of CNN draws much attention. And random forest (RF) algorithm, on the other hand,...

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Bibliographic Details
Main Authors: Luofan Dong, Huaqiang Du, Fangjie Mao, Ning Han, Xuejian Li, Guomo Zhou, Di'en Zhu, Junlong Zheng, Meng Zhang, Luqi Xing, Tengyan Liu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Classification
convolutional neural networks (CNNs)
random forest (RF)
subtropical forest
very high resolution remote sensing (VHRRS)
Online Access:https://ieeexplore.ieee.org/document/8935521/

Internet

https://ieeexplore.ieee.org/document/8935521/

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