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.

引用

页数：4

共 8 条

[1] Reducing the dimensionality of data with neural networks [J].