A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery

Jin Xing, Ru Luo (), Lifu Chen (), Jielan Wang (), Xingmin Cai (), Shuo Li, Phil Blythe, Yanghanzi Zhang and Simon Edwards
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Jin Xing: Newcastle University
Ru Luo: Changsha University of Science & Technology
Lifu Chen: Changsha University of Science & Technology
Jielan Wang: Changsha University of Science & Technology
Xingmin Cai: Changsha University of Science & Technology
Shuo Li: Newcastle University
Phil Blythe: Newcastle University
Yanghanzi Zhang: Newcastle University
Simon Edwards: Newcastle University

A chapter in Synthetic Aperture Radar (SAR) Data Applications, 2022, pp 91-111 from Springer

Abstract: Abstract Although numerous deep neural networks have been explored for aircraft detection using synthetic aperture radar (SAR) imagery, limited work has been conducted with their performance comparison, since different neural networks are designed and tested using different datasets and measured with different metrics. In this book chapter, we compare the performance of six popular deep neural networks for aircraft detection from SAR imagery, to verify their performance in tackling the scale heterogeneity, the background interference and the speckle noise challenges in the SAR-based aircraft detection. We choose SAR images acquired from three major airports in China as the testing datasets, due to the lack of ubiquitously agreed SAR benchmark dataset in aircraft detection. This comparison work does not only confirm the value of deep learning in aircraft detection but also highlights the advantages and disadvantages of these techniques, which paves the path for the design and development of workflow guidance in SAR-based aircraft detection using deep neural networks. It also serves as a baseline for future deep learning comparison in remote sensing data analytics, so as to facilitate the domain knowledge integration and design of innovative aircraft detection deep learning techniques.

Date: 2022
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DOI: 10.1007/978-3-031-21225-3_5

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