A Study of Morphological Pre-Processing Approaches for Track-Before-Detect Dim Target Detection

Lai, John S., Ford, Jason J., O'Shea, Peter J., Walker, Rodney A., & Bosse, Michael (2008) A Study of Morphological Pre-Processing Approaches for Track-Before-Detect Dim Target Detection. In Kim, Jonghyuk & Mahony, Robert (Eds.) 2008 Australasian Conference on Robotics & Automation, 3-5 December, 2008, Canberra.

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The track-before-detect processing technique has been employed in numerous computer vision based algorithms addressing the dim target detection problem. This processing technique has been shown to be effective under certain conditions; but in particularly noisy or highly cluttered environments, detection performance may be improved by introducing an image preprocessing stage to enhance the raw sensor measurements prior to integration. In this paper, we compare the 'Close-Minus-Open' (CMO) and 'Preserved-Sign' (PS) morphological image preprocessing techniques for suppressing unwanted noise and emphasising target features in the measurement images. This investigation is motivated by the unmanned aerial vehicle "sense-and-avoid" application, where morphology-based filters have demonstrated a degree of success in the detection of small pointlike features that may correspond to collision-course aircraft. For completeness, we also briefly examine two well published track-before-detect temporal filtering techniques which may be combined with the morphological pre-processing to detect dim, sub-pixel sized targets. Results from our simulation studies show that the PS approach achieves a higher detection rate than the CMO approach.

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ID Code: 16823
Item Type: Conference Paper
Refereed: Yes
Additional Information: The contents of this proceeding can be freely accessed online via the conference's web page (see hypertext link).
Keywords: Image Morphology, Morphological Filtering, Track-Before-Detect, Dim Target Detection
ISBN: 9780646506432
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2008 the authors
Deposited On: 11 Dec 2008 01:10
Last Modified: 22 Jun 2017 14:40

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