Research of Thermal Infrared Target Detection by Second Prediction Difference Method and Top-Hat Transformation

Abstract:

Detection technology of thermal infrared targets in scene image is to automatically detect target in busy background and intensive environmental noises by image processing algorithm and then to obtain the gray-scale targets image. Intensity of thermal radiation have business with temperature, so when the temperature is high then the radiation intensity is high and the image tone is shallow, but when temperature is low then the intensity is low and the tone is deep. This article researched the firelight in scene image, and combined the second prediction difference method and Top-Hat target detection based on gray-scale morphology to obtain the thermal infrared target image. Further more, it did simulation experiments and research deeply image processing technology according to the actual scene image. Finally it summarizes the effective algorithms of detection and identification which have instructional meaning to optical remote sensing, targets detection and fire control engineering and so on.

• Periodical: Applied Mechanics and Materials (Volumes 530-531)
• Pages: 289-292
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.530-531.289
• Online since: February 2014
• Authors: Fang Wang*, Lei Quan
• Keywords: Background Suppression, Grayscale Conversion, Second Prediction Difference, Target Detection, Thermal Infrared Target Image

References

[1] Huiying Gao, Jicheng Li, Zhenkang She. Research on predictive processing foreign small target detection. [J]. Infrared and laser engineering, 33(2): 154-158.

[2] Kesong Han. 2000. Processing infrared target detection under complicated background. [J]. Journal of system engineering and electronics, 22(l): 52一54.

[3] Zhaolin Sun. 2002. MATLAB6. x image processing. [M]. Beijing: Tsinghua University Press, 5.

[4] Ying Xu. 2005. Research of infrared small target detection methods. [J]. IR magazine, (5): 1一4.

[5] Chengqing Xie, Hong Xia, Zhu Li. 2011. Autoregression system identification model based on RBF neural network . [J]. Learned journal of ECIT, 4: 392-395.