基于深度学习的光学遥感机场与飞行器目标识别技术

牛新; 窦勇; 张鹏; 曹玉社

doi:10.11959/j.issn.2096-0271.2016054

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基于深度学习的光学遥感机场与飞行器目标识别技术

研究 | 更新时间：2024-06-03

- 基于深度学习的光学遥感机场与飞行器目标识别技术
- Airport and flight recognition on optical remote sensing data by deep learning
- 大数据 2016年2卷第5期页码：2016054
- 作者机构：
- 作者简介：
  
  [ "牛新（1983-），男，博士，国防科学技术大学并行与分布处理重点实验室助理研究员，主要研究方向为计算机系统结构、遥感图像处理、机器学习。" ]
  [ "窦勇（1966-），男，博士，国防科学技术大学并行与分布处理重点实验室常务副主任、研究员、博士生导师，计算机系统结构学科方向学术带头人，担任中国计算机学会体系结构专家委员会主任，是国家自然科学基金杰出青年基金获得者，入选教育部新世纪优秀人才支持计划。主要研究方向为计算机系统结构、并行计算、计算机应用。" ]
  [ "张鹏（1993-），男，国防科学技术大学并行与分布处理重点实验室硕士生，主要研究方向为计算机系统结构、遥感图像处理、机器学习。" ]
  [ "曹玉社（1990-），男，国防科学技术大学并行与分布处理重点实验室硕士生，主要研究方向为计算机系统结构、遥感图像处理、机器学习。" ]
- 基金信息：
  
  ：国家自然科学基金资助项目;The National Natural Science Foundation of China(U1435219);：国家自然科学基金资助项目;The National Natural Science Foundation of China(61402507)
- DOI：10.11959/j.issn.2096-0271.2016054
  中图分类号： TP183
- 网络首发：2016-09，
  
  纸质出版：2016-09-20
- 稿件说明：
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牛新, 窦勇, 张鹏, 等. 基于深度学习的光学遥感机场与飞行器目标识别技术[J]. 大数据, 2016,2(5):2016054.

Xin NIU, Yong DOU, Peng ZHANG, et al. Airport and flight recognition on optical remote sensing data by deep learning[J]. Big data research, 2016, 2(5): 2016054.
牛新, 窦勇, 张鹏, 等. 基于深度学习的光学遥感机场与飞行器目标识别技术[J]. 大数据, 2016,2(5):2016054. DOI： 10.11959/j.issn.2096-0271.2016054.

Xin NIU, Yong DOU, Peng ZHANG, et al. Airport and flight recognition on optical remote sensing data by deep learning[J]. Big data research, 2016, 2(5): 2016054. DOI： 10.11959/j.issn.2096-0271.2016054.

摘要

机场与飞行器目标识别是遥感数据分析中的典型应用。研究了光学遥感大数据环境下面向机场与飞行器目标识别的深度学习技术。为此，构建了一个面向高分光学遥感图像的机场与飞行器目标秒级识别系统。使用迁移学习的方法在有标签样本稀缺的情况下有效构建深度网络，利用目标先验知识对潜在目标进行高效提取，并提出一种层次式的级联深度网络识别架构，实现“大范围、小目标”的实时识别。实验结果表明，采用相应技术，基于深度学习方法可以在秒级时间得到比传统方法更高的识别精度。

Abstract

Airport and flight recognition are the typical remote sensing applications.For the big optical remote sensing data

deep learning techniques for airport and flight recognition have been studied.To this end

a seconds’ response airport and flight recognition system for optical remote sensing data was built.To obtain effective deep learning model with limited labeled samples

transfer learning approach has been employed.Prior knowledge has also been explored for efficient object proposal.To achieve real-time performance for such recognition with “large region and small targets”

a cascade framework of deep networks has been proposed.The results of experiments show that

by the proposed deep learning approaches

significant improvement on recognition accuracy could be achieved with seconds’ response.

关键词

Keywords

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