Research on simulated infrared image utility evaluation using deep representation

Ruiheng Zhang; Chengpo Mu; Yu Yang; Lixin Xu

doi:10.1117/1.JEI.27.1.013012

26 January 2018 Research on simulated infrared image utility evaluation using deep representation

Ruiheng Zhang, Chengpo Mu, Yu Yang, Lixin Xu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 1, 013012 (January 2018). https://doi.org/10.1117/1.JEI.27.1.013012

Abstract

Infrared (IR) image simulation is an important data source for various target recognition systems. However, whether simulated IR images could be used as training data for classifiers depends on the features of fidelity and authenticity of simulated IR images. For evaluation of IR image features, a deep-representation-based algorithm is proposed. Being different from conventional methods, which usually adopt a priori knowledge or manually designed feature, the proposed method can extract essential features and quantitatively evaluate the utility of simulated IR images. First, for data preparation, we employ our IR image simulation system to generate large amounts of IR images. Then, we present the evaluation model of simulated IR image, for which an end-to-end IR feature extraction and target detection model based on deep convolutional neural network is designed. At last, the experiments illustrate that our proposed method outperforms other verification algorithms in evaluating simulated IR images. Cross-validation, variable proportion mixed data validation, and simulation process contrast experiments are carried out to evaluate the utility and objectivity of the images generated by our simulation system. The optimum mixing ratio between simulated and real data is 0.2≤γ≤0.3, which is an effective data augmentation method for real IR images.

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Ruiheng Zhang, Chengpo Mu, Yu Yang, and Lixin Xu "Research on simulated infrared image utility evaluation using deep representation," Journal of Electronic Imaging 27(1), 013012 (26 January 2018). https://doi.org/10.1117/1.JEI.27.1.013012

Received: 8 October 2017; Accepted: 3 January 2018; Published: 26 January 2018

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