Imaging Components, Systems, and Processing

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

[+] Author Affiliations
Sungho Kim

Yeungnam University, Department of Electronic Engineering, Gyeongsan-si, Gyeongsangbuk-do, Republic of Korea

Opt. Eng. 56(6), 063108 (Jun 29, 2017). doi:10.1117/1.OE.56.6.063108
History: Received March 2, 2017; Accepted June 8, 2017
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Abstract.  Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Sungho Kim
"Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition", Opt. Eng. 56(6), 063108 (Jun 29, 2017). ; http://dx.doi.org/10.1117/1.OE.56.6.063108


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