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Proceedings Paper

Algorithm fusion in forward-looking long-wave infrared imagery for buried explosive hazard detection
Author(s): D. T. Anderson; James M. Keller; Ozy Sjahputera
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Paper Abstract

In this article, we propose a method to fuse multiple algorithms in a long wave infrared (LWIR) system in the context of forward looking buried explosive hazard detection. A pre-screener is applied first, which is an ensemble of local RX filters and mean shift clustering in UTM space. Hit correspondence is then performed with an algorithm based on corner detection, local binary patterns (LBP), multiple instance learning (MIL) and mean shift clustering in UTM space. Next, features from image chips are extracted from UTM confidence maps based on maximally stable extremal regions (MSERs) and Gaussian mixture models (GMMs). These sources are then fused using an ordered weighted average (OWA). While this fusion approach has yet to improve the overall positive detection rate in LWIR, we do demonstrate false alarm reduction. Targets that are not detected by our system are also not detected by a human under visual inspection. Experimental results are shown based on field data measurements from a US Army test site.

Paper Details

Date Published: 23 May 2011
PDF: 12 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 801722 (23 May 2011); doi: 10.1117/12.884600
Show Author Affiliations
D. T. Anderson, Univ. of Missouri (United States)
James M. Keller, Univ. of Missouri (United States)
Ozy Sjahputera, Univ. of Missouri (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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