Full-polarimetric scattering characteristics prediction from single/dual-polarimetric SAR data using convolutional neural networks

Full-polarimetric scattering characteristics prediction from single/dual-polarimetric SAR data using convolutional neural networks

Scattering entropy (H), scattering angle (α) and anti-entropy (A) are useful parameters in synthetic aperture radar (SAR) image classification. Usually, full-polarimetric SAR data are needed to extract these parameters. In this study, the authors firstly try to predict these parameters from single/d...

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Bibliographic Details
Main Authors: Juan Zhang, Xiaolan Qiu, Xiangfeng Wang, Yan Jin
Format: Article
Language:English
Published: Wiley 2019-10-01
Series:The Journal of Engineering
Subjects:
image classification
radar polarimetry
synthetic aperture radar
radar imaging
convolutional neural nets
radar computing
sar image classification
scattering entropy
single-dual-polarimetric sar data
full-polarimetric scattering characteristic prediction
antientropy
full-polarimetric sar data
synthetic aperture radar image classification
predictive parameters
average relative error
gf-3 polarised sar database
convolutional neural network
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0563

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0563

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