Next Article in Journal
Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes
Next Article in Special Issue
An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing
Previous Article in Journal
Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)
Previous Article in Special Issue
Effective Fusion of Multi-Modal Remote Sensing Data in a Fully Convolutional Network for Semantic Labeling
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(1), 139; https://doi.org/10.3390/rs10010139

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

1,2,3, 1,2,3 and 1,2,3,*
1
School of Automation, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China
2
National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Luoyu Road 1037, Wuhan 430074, China
3
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]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top