Prof. Liguo Zhu Profile

Prof. Liguo Zhu

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Proceedings Article | 13 December 2024 Paper

Terahertz spectral material recognition in online postal packages based on generative adversarial networks

Yao Xu, Weiwei Yu, Yanan Wen, Kun Meng, Zeren Li, Liguo Zhu

Proceedings Volume 13495, 134950G (2024) https://doi.org/10.1117/12.3046838

KEYWORDS: Terahertz technology, Absorption, Data modeling, Education and training, Gallium nitride, Terahertz spectroscopy, Inspection, Deep learning, Absorption spectrum, Packaging

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Terahertz technology is the only technology that can achieve specific identification of hazardous chemicals, drugs, explosives, and other contraband inside mail packages without opening them. Therefore, overcoming the challenges of terahertz postal security inspection technology has broad market application prospects and extremely important social significance. In recent years, the combination of terahertz time-domain spectroscopy and deep learning has been widely applied in the field of material identification. However, in practical applications, based on the characteristic absorption spectra in the frequency range, the terahertz absorption spectra of amino acids vary with different packaging materials. Substance classification algorithms based on deep learning and machine learning show high accuracy in offline data models but lower accuracy during real-time online detection. Real-time detection of online amino acid samples based on terahertz time-domain spectroscopy technology should fundamentally solve these issues by increasing the training data, i.e., generating more data from the raw data. Generative adversarial networks (GANs) are a type of deep learning model that can learn the complex distribution of raw data. However, in the field of terahertz material identification, GANs have rarely been used to generate data to improve classifier performance. Therefore, this paper proposes a data augmentation method based on GANs. Then, a terahertz spectrum classification technique combining decision tree (DT), support vector machine (SVM), and convolutional neural network (CNN) is used to identify terahertz spectra within packages.

Proceedings Article | 21 July 2017 Paper

A novel remote sensing image classification algorithm based on multi-feature optimization and TWSVM

Zhiqiang Liu, Liguo Zhu

Proceedings Volume 10420, 104203F (2017) https://doi.org/10.1117/12.2281996

KEYWORDS: Remote sensing, Image classification, Optimization (mathematics), Principal component analysis

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For remote sensing images with rich content of features, this paper presents a remote sensing image classification algorithm based on twin support vector machine (TWSVM) and multi-feature optimization. Firstly, we extract color feature and shape feature of remote sensing image and introduce the local angular phase (LAP) histogram, which has high texture description ability, as the texture feature. Due to these three kinds of feature represent different emphases of remote sensing image; the reasonable combination of them can be more comprehensive description of the contents of remote sensing image. Secondly, we use kernel principal component analysis (KPCA) to reduce the dimension of every kind of feature, and construct reasonable feature space based on different weights obtained by the distribution of feature space. Finally, the remote sensing image samples classification and test is completed in the TWSVM model that has better classification performance. Experimental results on the USGS test-set show that, the average classification accuracy of the proposed algorithm is reached to 93.7% compared with three popular methods. Compared with the highest classification accuracy of single feature classification algorithm, the average classification accuracy of the proposed algorithm has been improved by 16%, 14.5% and 9.2%.

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