Author
Listed:
- 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)
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.
Suggested Citation
Jin Xing & Ru Luo & Lifu Chen & Jielan Wang & Xingmin Cai & Shuo Li & Phil Blythe & Yanghanzi Zhang & Simon Edwards, 2022.
"A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery,"
Springer Optimization and Its Applications, in: Maciej Rysz & Arsenios Tsokas & Kathleen M. Dipple & Kaitlin L. Fair & Panos M. Pardalos (ed.), Synthetic Aperture Radar (SAR) Data Applications, pages 91-111,
Springer.
Handle:
RePEc:spr:spochp:978-3-031-21225-3_5
DOI: 10.1007/978-3-031-21225-3_5
Download full text from publisher
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below whether another version of this item is available online.
2. Check on the provider's
web page
whether it is in fact available.
3. Perform a
search for a similarly titled item that would be
available.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-3-031-21225-3_5. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.