Extracting peak statistics with recurrent network for detecting dim point-source targets in IR noise

Guan Hua; Lan Tao; Zhenkang Shen; Zhongkang Sun

doi:10.1117/12.234256

4 March 1996 Extracting peak statistics with recurrent network for detecting dim point-source targets in IR noise

Guan Hua, Lan Tao, Zhenkang Shen, Zhongkang Sun

Proceedings Volume 2664, Applications of Artificial Neural Networks in Image Processing; (1996) https://doi.org/10.1117/12.234256
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

In this paper, peak-statistics-based preprocessing methods for detecting point-source target (PST) in IR images are described, and a neural network connection for extracting peak- statistics with recurrent networks is presented. Because of the strong correlation in IR noise, a sample will have less probability in having a larger intensity than its adjacent samples do, in other words, it will have less probability in becoming a peak. The presence of PST interrupts the consistency of correlate between adjacent samples of IR noise, as a result, turning up the difference in peak-statistics. Based on the features of PST and a detailed analysis on the features of IR noise, we adopt a modified difference operation, namely, bidirectional difference (BD), and a peak-statistics-based threshold operation as the preprocessing step. Theory analyses and simulation results have shown that the performance of the proposed methods is better than normal threshold operation.

Citation Download Citation

Guan Hua, Lan Tao, Zhenkang Shen, and Zhongkang Sun "Extracting peak statistics with recurrent network for detecting dim point-source targets in IR noise", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); https://doi.org/10.1117/12.234256

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