A Review on Image Classification of Remote Sensing Using Deep Learning

被引:0
|
作者
Yao, Chuchu [1 ]
Luo, Xianxian [1 ,2 ,3 ]
Zhao, Yudan [1 ]
Zeng, Wei [1 ,2 ,3 ]
Chen, Xiaoyu [4 ]
机构
[1] Quanzhou Normal Univ, Fac Math & Comp Sci, Quanzhou, Peoples R China
[2] Fujian Prov Univ, Fujian Prov Key Lab Data Intens Comp, Quanzhou 362000, Peoples R China
[3] Fujian Prov Univ, Key Lab Intelligent Comp & Informat Proc, Quanzhou 362000, Peoples R China
[4] Coll Resource & Environm Sci, Wuhan, Hubei, Peoples R China
关键词
remote sensing; deep learning; image classification; SPECTRAL-SPATIAL CLASSIFICATION; AUTOENCODER;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Deep learning is the state-of-the-art of machine learning. Previous literature demonstrates that deep learning gains the excellent performance in image classification of remote sensing. To begin with, data sources of remote sensing and current classification methods are briefly introduced. Then, common data sets and typical models of deep learning are presented, including deep belief network, convolutional neural network, stacked auto encoder. Furthermore, optimal configuration of these methods of deep learning is summarized according to the overall accuracy and Kappa coefficient. Finally, the existing problems and future work of satellite images classification by deep learning are pointed out. The review shows that deep learning is promised to be dominant method of image classification of remote sensing.
引用
收藏
页码:1947 / 1955
页数:9
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