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Remote Sens. 2018, 10(1), 139;

End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images

1,2,3, 1,2,3 and 1,2,3,*
School of Automation, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China
National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Luoyu Road 1037, Wuhan 430074, China
Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Luoyu Road 1037, Wuhan 430074, China
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 6 January 2018 / Accepted: 15 January 2018 / Published: 18 January 2018
PDF [8383 KB, uploaded 18 January 2018]


Airplane detection in remote sensing images remains a challenging problem due to the complexity of backgrounds. In recent years, with the development of deep learning, object detection has also obtained great breakthroughs. For object detection tasks in natural images, such as the PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) VOC (Visual Object Classes) Challenge, the major trend of current development is to use a large amount of labeled classification data to pre-train the deep neural network as a base network, and then use a small amount of annotated detection data to fine-tune the network for detection. In this paper, we use object detection technology based on deep learning for airplane detection in remote sensing images. In addition to using some characteristics of remote sensing images, some new data augmentation techniques have been proposed. We also use transfer learning and adopt a single deep convolutional neural network and limited training samples to implement end-to-end trainable airplane detection. Classification and positioning are no longer divided into multistage tasks; end-to-end detection attempts to combine them for optimization, which ensures an optimal solution for the final stage. In our experiment, we use remote sensing images of airports collected from Google Earth. The experimental results show that the proposed algorithm is highly accurate and meaningful for remote sensing object detection. View Full-Text
Keywords: airplane detection; end to end; transfer learning; convolutional neural networks airplane detection; end to end; transfer learning; convolutional neural networks

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Chen, Z.; Zhang, T.; Ouyang, C. End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images. Remote Sens. 2018, 10, 139.

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