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http://dx.doi.org/10.9766/KIMST.2017.20.6.758

A Design of Du-CNN based on the Hybrid Machine Characters to Classify Target and Clutter in The IR Image  

Lee, Juyoung (The 3rd Research and Development Institute, Agency for Defense Development)
Lim, Jaewan (The 3rd Research and Development Institute, Agency for Defense Development)
Baek, Haeun (The 3rd Research and Development Institute, Agency for Defense Development)
Kim, Chunho (The 1st Research and Development Institute, Agency for Defense Development)
Park, Jungsoo (The 1st Research and Development Institute, Agency for Defense Development)
Koh, Eunjin (The 3rd Research and Development Institute, Agency for Defense Development)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.20, no.6, 2017 , pp. 758-766 More about this Journal
Abstract
In this paper, we propose a robust duality of CNN(Du-CNN) method which can classify the target and clutter in coastal environment for IR Imaging Sensor. In coastal environment, there are various clutter that have many similarities with real target due to diverse change of air temperature, water temperature, weather and season. Also, real target have various feature due to the same reason. Thus, the proposed Du-CNN method adopts human's multiple personality utilization and CNN technique to learn and classify target and clutter. This method has an advantage of the real time operation. Experimental results on sampled dataset of real infrared target and clutter demonstrate that the proposed method have better success rate to classify the target and clutter than general CNN method.
Keywords
Infrared Image; Convolutional Neural Network; Target Classification; Machine Learning; Multiple Personality;
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