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

• 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

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/dual-polarimetric SAR data using convolutional neural network. Experiments are done on GF-3 polarised SAR database, and promising results are obtained, where the parameters H and α, the average relative error reached is <10%, the parameter A, the average relative error reached is around 25%, and the classification performance based on predictive parameters is around 80%. Furthermore, the predicting performance using different single- and dual-polarisation is compared. The results and conclusions provide a new clue for the applications of single/dual-polarimetric SAR.