A robust FLIR target detection employing an auto-convergent pulse coupled neural network

Journal article


Dey, M., Rana, S. and Siarry, P. (2019). A robust FLIR target detection employing an auto-convergent pulse coupled neural network. Remote Sensing Letters. 10 (7), pp. 639-648.
AuthorsDey, M., Rana, S. and Siarry, P.
Abstract

© 2019 Informa UK Limited, trading as Taylor & Francis Group. Automatic target detection (ATD) of a small target along with its true shape from highly cluttered forward-looking infrared (FLIR) imagery is crucial. FLIR imagery is low contrast in nature, which makes it difficult to discriminate the target from its immediate background. Here, pulse-coupled neural network (PCNN) is extended with auto-convergent criteria to provide an efficient ATD tool. The proposed auto-convergent PCNN (AC-PCNN) segments the target from its background in an adaptive manner to identify the target region when the target is camouflaged or contains higher visual clutter. Then, selection of region of interest followed by template matching is augmented to capture the accurate shape of a target in a real scenario. The outcomes of the proposed method are validated through well-known statistical methods and found superior performance over other conventional methods.

Year2019
JournalRemote Sensing Letters
Journal citation10 (7), pp. 639-648
PublisherTaylor & Francis
Digital Object Identifier (DOI)doi:10.1080/2150704X.2019.1597296
Web address (URL)https://www.tandfonline.com/doi/full/10.1080/2150704X.2019.1597296
Publication dates
Online27 Mar 2019
Publication process dates
Accepted14 Mar 2019
Deposited29 Aug 2019
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/87yz1

Accepted author manuscript

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