Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network

Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network

The deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its powerful feature extraction capabilities. It is well-known that huge high-quality labeled datasets are required for achieving...

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Bibliographic Details
Main Authors: Wenmei Li, Ziteng Wang, Yu Wang, Jiaqi Wu, Juan Wang, Yan Jia, Guan Gui
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep convolutional neural network (DeCNN)
few shot
high-spatial-resolution remote sensing (HSRRS)
scene classification
transfer learning
Online Access:https://ieeexplore.ieee.org/document/9085918/

Internet

https://ieeexplore.ieee.org/document/9085918/

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