CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor杨一平 ; 马雷
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Name工程硕士
Degree Discipline计算机技术
Keyword遥感图像 目标检测 深度学习 旋转矩形框 显著性 飞机识别



1. 飞机目标显著性区域检测算法研究


2. 基于朝向矩形框的飞机检测算法研究


3. 飞机细粒度分类算法研究


Other Abstract

Aircraft detection and recognition in remote sensing images is one of the research hotspots of remote sensing object detection. With the increase of the resolution of satellite sensors, the image details are becoming more and more abundant, which makes high-precision aircraft detection and recognition possible. At the same time, the accumulated massive images are difficult for human to interpret. Therefore, deep-learning based methods are becoming more and more important.

After an in-depth analysis of the present relevant literatures on aircraft detection and recognition algorithms and summaries of the shortcomings existing work, this paper conducts research on improving the efficiency and accuracy of aircraft detection. The main research content includes the following points:

1. Research on object saliency detection 

In order to improve the speed of objection detection, a Shuttle-Net-based remote sensing image aircraft saliency region filtering algorithm is implemented. The designed model has fewer parameters, faster detection speed and higher accuracy.

2. Research on oriented-bounding-box based aircraft detection

with the nature of rigid transformation of aircraft in remote sensing images, a new object representation method of oriented bounding box is proposed. Combined with the classical detection model, a new aircraft detection model is proposed. Compared with previous methods, the proposed method can not only give the position information of the aircraft, but also give the orientation angle of the aircraft and has higher precision.

3. Research on Fine-grained aircraft classification

In order to realize the fine-grained classification of aircraft models, a new remote sensing image military aircraft data set is established. According to the detection result of the oriented-bounding-box based detection model, a fine-grained classification model of aircraft angle alignment is designed. Compared with the model without angle alignment, the proposed method has higher classification accuracy.

Document Type学位论文
Recommended Citation
GB/T 7714
蔡健. 基于深度学习的遥感图像飞机检测与识别研究[D]. 中国科学院自动化研究所. 中国科学院大学,2019.
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