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

Authors : H. Seçkin Demir, Erdem Akagündüz

Pages : 302-317

Doi:10.3906/elk-1807-287

View : 8 | Download : 7

Publication Date : 9999-12-31

Article Type : Makaleler

Abstract :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.
Keywords : Small target detection, filter design, normalized-cross-correlation, convolutional neural networks