基于深度学习的光学遥感机场与飞行器目标识别技术

doi:10.11959/j.issn.2096-0271.2016054

Abstract

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.

Key words: optical remote sensing, object recognition, deep learning

Xin NIU, Yong DOU, Peng ZHANG, Yushe CAO. Airport and flight recognition on optical remote sensing data by deep learning[J]. Big Data Research, 2016, 2(5): 54-67.

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候选方法	准确率	虚警率	平均耗时/s
基于显著性区域候选方法^[28]+CNN	68.5%	5%	3.85
基于显著性区域候选方法^[28]+SIFT	52%	8.5%	8.57
笔者提出的区域候选方法+ 8 纹理特征SVM^[29]	62%	7.5%	68.64
笔者提出的区域候选方法+ SIFT	63%	15%	64.09
笔者提出的区域候选方法+ CNN	87%	12%	21.3

Airport and flight recognition on optical remote sensing data by deep learning

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