Complex Scene Classification of High Resolution Remote Sensing Images Based on DCNN Model

被引：2
|
作者：
Chen, Dexi [1 ]
Hu, Peng [1 ]
Duan, Xuelin [2 ]
机构：
[1] China Ship Dev & Design Ctr, Wuhan, Peoples R China
[2] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Peoples R China
来源：
2019 10TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTITEMP) | 2019年
关键词：
high resolution remote sensing image; scene classification; deep convolutional neural network;
D O I：
10.1109/multi-temp.2019.8866895
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Aiming at the problem that the traditional scene classification methods are not accurate to the semantic description of high resolution remote sensing images, a method based on deep convolutional neural network(DCNN) is proposed. It achieves an accuracy of 93% on the dataset (whu-6) made by myself, which significantly improves the classification accuracy compared with the traditional scene classification methods.
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页数：4
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