Remote Sensing Image Object Recognition Based on Convolutional Neural Network

被引：0

作者：

Zhen, Yumei ^{[1
]}

Liu, Huanyu ^{[1
]}

Li, Junbao ^{[1
]}

Hu, Cong ^{[2
]}

Pan, Jeng-Shyang ^{[3
]}

机构：

[1] Harbin Inst Technol, Dept Automat Test & Control, Harbin 150080, Heilongjiang, Peoples R China

[2] Guilin Univ Elect Technol, Sch Elect Engn & Automat, Guilin 541004, Peoples R China

[3] Fujian Univ Technol, Fujian Prov Key Lab Big Data Min & Applicat, Fuzhou 350108, Fujian, Peoples R China

来源：

PROCEEDINGS FIRST INTERNATIONAL CONFERENCE ON ELECTRONICS INSTRUMENTATION & INFORMATION SYSTEMS (EIIS 2017) | 2017年

基金：

美国国家科学基金会;

关键词：

deep learning; object recognition; pre-train;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The development of space remote sensing technology brings a lot of remote sensing image data. The traditional target detection method is difficult to adapt to the large amount of high-resolution remote sensing image data. It is necessary to find a way to automatically learn the most effective features from the image data, and to fully recover the correlation between the data. Based on the recognition of the typical targets in remote sensing image data, this paper proposes a method of remote sensing target recognition based on deep learning. In this paper, the pre-train method is introduced to improve the simulation of the model. The experiment of the test set proves the validity of the method.

引用

页码：814 / 817

页数：4

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