Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
n 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.
DOI
10.3906/elk-1807-287
Keywords
Small target detection, filter design, normalized-cross-correlation, convolutional neural networks
First Page
302
Last Page
317
Recommended Citation
DEMİR, H. S, & AKAGÜNDÜZ, E (2020). Filter design for small target detection on infrared imagery using normalized-cross-correlation layer. Turkish Journal of Electrical Engineering and Computer Sciences 28 (1): 302-317. https://doi.org/10.3906/elk-1807-287
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Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
