@inproceedings{82e537e9315f4957be9721ef41ba3320,
title = "A Deep Convolutional Neural Networks-Based Method for Inversion of Rough Surface Parameters",
abstract = "Deep convolution networks (CNN) is applied to inverse the rough surface parameters, including the root-me an-square height and the correlation length, from microwave images. We employ computational electromagnetic method to simulate the training data for deep CNN. The simulated backward scattering data is converted into microwave images as the inputs to the CNN. An inversion network of deep convolution neural networks with five cascaded convolutional-maxpooling layers and two fully connected layers is designed, including feature extraction and data regression by using convolution layers and fully connected layers. The simulated results demonstrate the feasibility to inverse the sough surface parameters from electromagnetic scattering fields by using deep CNN.",
keywords = "deep convolutional neural networks, inversion, microwave image, rough surface back scattering",
author = "Lingyan Han and Lei Kuang and Tao Song and Liu, \{Qing Huo\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Computational Electromagnetics, ICCEM 2018 ; Conference date: 26-03-2018 Through 28-03-2018",
year = "2018",
month = oct,
day = "17",
doi = "10.1109/COMPEM.2018.8496583",
language = "英语",
series = "2018 IEEE International Conference on Computational Electromagnetics, ICCEM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on Computational Electromagnetics, ICCEM 2018",
address = "美国",
}