Filter design for small target detection on infrared imagery using normalized-cross-correlation layer

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2020-01-01

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Demir, H. Seckin
Akagündüz, Erdem

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In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similar to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on midwave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.

URI

https://hdl.handle.net/11511/93552

Journal

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES

DOI

https://doi.org/10.3906/elk-1807-287

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Graduate School of Informatics, Article

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Citation Formats

H. S. Demir and E. Akagündüz, “Filter design for small target detection on infrared imagery using normalized-cross-correlation layer,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol. 28, no. 1, pp. 302–317, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93552.