scholarly journals Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

2018 ◽  
Vol 10 (3) ◽  
pp. 443 ◽  
Author(s):  
Fen Chen ◽  
Ruilong Ren ◽  
Tim Van de Voorde ◽  
Wenbo Xu ◽  
Guiyun Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Remote Sensing Images ◽  
Airport Detection

scholarly journals End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks

2018 ◽  
Vol 10 (10) ◽  
pp. 1516 ◽  
Author(s):  
Yuelei Xu ◽  
Mingming Zhu ◽  
Shuai Li ◽  
Hongxiao Feng ◽  
Shiping Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
False Alarm ◽  
Convolutional Neural Networks ◽  
Detection Rate ◽  
Detection Methods ◽  
Detection Time ◽  
Remote Sensing Images ◽  
Threshold Detection ◽  
Airport Detection

Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of “divide and conquer” and “integral loss’’ are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.

Object Detectionin of Remote Sensing Images Based on Convolutional Neural Networks

2019 ◽  
Vol 56 (5) ◽  
pp. 051002
Author(s):  
欧攀 Ou Pan ◽  
张正 Zhang Zheng ◽  
路奎 Lu Kui ◽  
刘泽阳 Liu Zeyang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Remote Sensing Images

Vehicle detection in remote sensing images using denoizing-based convolutional neural networks

2016 ◽  
Vol 8 (3) ◽  
pp. 262-270 ◽  
Author(s):  
Hao Li ◽  
Kun Fu ◽  
Menglong Yan ◽  
Xian Sun ◽  
Hao Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Detection ◽  
Remote Sensing Images
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