SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing

被引：5

作者：

Wu, Chunxue ^{[1
]}

Ju, Bobo ^{[1
]}

Wu, Yan ^{[2
]}

Xiong, Naixue ^{[1
,3
]}

机构：

[1] Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai 200093, Peoples R China

[2] Indiana Univ Bloomington, ONeill Sch Publ & Environm Affairs, Bloomington, IN 47405 USA

[3] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, CO 74464 USA

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

SlimRGBD; ResNet; generative adversarial networks; image noise reduction; UAV; channel pruning; sparse training; IMAGE; ALGORITHM;

D O I：

10.1109/ACCESS.2020.2966497

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the past ten years, civil drone technology has developed rapidly, and UAV (Unmanned Aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UAV technology has enabled the geographical information of remote areas that are not concerned to be quickly presented. However, UAV aerial photography is greatly affected by the weather. Pictures that use aerial drones for aerial photography in rainy weather will appear noise. In this paper, how to eliminate the noise of aerial image is to be talked, the multi-channel pruning technology is used to pruning the RnResNet network. Based on this, a new anti-convergence-convolution neural network noise reduction system for the operation of UAV airborne embedded equipment is proposed. The system is used to eliminate noise in the aerial image. This type of noise reducer has got rid of the current situation that the neural network noise reducer consumes too much power and is inefficient, and has certain advantages.

引用

页码：15144 / 15158

页数：15

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