Synthetic Aperture Radar (SAR) Data Applications

Edited by Maciej Rysz (), Arsenios Tsokas (), Kathleen M. Dipple (), Kaitlin L. Fair () and Panos M. Pardalos ()

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2022
ISBN: 978-3-031-21225-3
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Chapters in this book:

End-to-End ATR Leveraging Deep Learning

Matthew P. Masarik, Chris Kreucher, Kirk Weeks and Kyle Simpson

Change Detection in SAR Images Using Deep Learning Methods

Luca Bergamasco and Francesca Bovolo

Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval

Seonho Park, Maciej Rysz, Kathleen M. Dipple and Panos M. Pardalos

Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks

Alexander Semenov, Maciej Rysz and Garrett Demeyer

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

Machine Learning Methods for SAR Interference Mitigation

Yan Huang, Lei Zhang, Jie Li, Mingliang Tao, Zhanye Chen and Wei Hong

Classification of SAR Images Using Compact Convolutional Neural Networks

Mete Ahishali, Serkan Kiranyaz and Moncef Gabbouj

Multi-Frequency Polarimetric SAR Data Analysis for Crop Type Classification Using Random Forest

Siddharth Hariharan, Dipankar Mandal, Siddhesh Tirodkar, Vineet Kumar and Avik Bhattacharya

Automatic Determination of Different Soil Types via Several Machine Learning Algorithms Employing Radarsat-2 SAR Image Polarization Coefficients

Emrullah Acar and Mehmet Sirac Ozerdem

Ocean and Coastal Area Information Retrieval Using SAR Polarimetry

Andrea Buono, Emanuele Ferrentino, Yu Li and Carina Regina de Macedo

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DOI: 10.1007/978-3-031-21225-3

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