Target recognition in synthetic aperture radar image based on PCANet
Author:
Affiliation:
1. Beijing Key Laboratory of Embedded Real‐time Information Processing TechnologyBeijing Institute of TechnologyBeijing 100081People's Republic of China
2. Shanghai Institute of Satellite EngineeringShanghai 200240People's Republic of China
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
General Engineering,Energy Engineering and Power Technology,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2019.0238
Reference14 articles.
1. Deep learning in remote sensing: A comprehensive review and list of resources;Zhu X.X.;IEEE Geosci. Remote Sens. Mag.,2017
2. Recent trend and advance of synthetic aperture radar with selected topics;Ouchi K.;Remote Sens.‐Basel,2013
3. Automatic target recognition in synthetic aperture radar imagery: a state‐of‐the‐art review;El‐Darymli K.;IEEE. Access.,2016
4. A novel hierarchical ship classifier for COSMO‐SkyMed SAR data;Wang C.;IEEE Geosci. Remote Sens. Lett.,2014
5. Target recognition in SAR images by exploiting the azimuth sensitivity;Ding B.;Remote. Sens. Lett.,2017
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