Landmine-detection prescreeners based on feature-level fusion of SAR and HSI data

Kenneth Ranney

doi:10.1117/12.720742

7 May 2007 Landmine-detection prescreeners based on feature-level fusion of SAR and HSI data

Kenneth Ranney

Proceedings Volume 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII; 65531Z (2007) https://doi.org/10.1117/12.720742
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Many automatic target detection (ATD) algorithm suites include a prescreener as an initial link in their processing chains. It assists the downstream algorithms by eliminating many potential false alarms while still retaining a large percentage of the objects of interest, thereby allowing for greater specialization by the downstream algorithms. Many such prescreeners have been implemented for individual sensors-for example the constant false alarm rate detectors of radar systems or the RX (Reed-Xiaoli) detection algorithms of hyperspectral imaging (HSI) systems. In this paper we examine straightforward methods for fusing the outputs from synthetic aperture radar (SAR) and HSI prescreeners to create a multi-sensor prescreening algorithm. We begin by examining the sensor phenomenology for a specific operational scenario, and we incorporate this phenomenological information into both the individual sensor prescreener designs and the final fusion algorithm design. We describe how the SAR and HSI prescreener detects are associated with one another prior to fusion. Finally, we describe multiple fusion methodologies-namely a method based on the Dempster-Shafer theory of evidence and a method based on a Bayesian approach under an assumption of independence. We compare results from each fusion algorithm with those obtained using a single sensor.

Citation Download Citation

Kenneth Ranney "Landmine-detection prescreeners based on feature-level fusion of SAR and HSI data", Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65531Z (7 May 2007); https://doi.org/10.1117/12.720742

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