Browse by author
Lookup NU author(s): Dr Jin XingORCiD, Dr Shuo LiORCiD, Professor Phil BlytheORCiD, Dr Yanghanzi ZhangORCiD, Simon Edwards
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.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.
Author(s): Xing J, Luo R, Chen L, Wang J, Cai X, Li S, Blythe P, Zhang Y, Edwards S
Editor(s): Rysz M; Tsokas A; Dipple KM; Fair KL; Pardalos PM
Publication type: Book Chapter
Publication status: Published
Book Title: Synthetic Aperture Radar (SAR) Data Applications
Year: 2023
Volume: 199
Pages: 91-111
Print publication date: 19/01/2023
Online publication date: 19/01/2023
Acceptance date: 02/04/2022
Series Title: Springer Optimization and Its Applications
Publisher: Springer
Place Published: Cham
URL: https://doi.org/10.1007/978-3-031-21225-3_5
DOI: 10.1007/978-3-031-21225-3_5
Library holdings: Search Newcastle University Library for this item
ISBN: 9783031212246