Computational intelligence methods in forward-looking explosive hazard detection
Document Type
Book Chapter
Publication Date
12-20-2015
Department
Department of Electrical and Computer Engineering; Center for Data Sciences
Abstract
This chapter discusses several methods for forward-looking (FL) explosive hazard detection (EHD) using FL infrared (FLIR) and FL ground penetrating radar (FLGPR). The challenge in detecting explosive hazards with FL sensors is that there are multiple types of targets buried at different depths in a highly-cluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Recent work in this application has focused on fusion methods, including fusion of multiple modalities of sensors (e.g., GPR and IR), fusion of multiple frequency sub-band images in FLGPR, and feature-level fusion using multiple kernel and iECO learning. For this chapter, we will demonstrate several types of EHD techniques, including kernel methods such as support vector machines (SVMs), multiple kernel learning MKL, and feature learning methods, including deep learners and iECO learning. We demonstrate the performance of several algorithms using FLGPR and FLIR data collected at a US Army test site. The summary of this work is that deep belief networks and evolutionary approaches to feature learning were shown to be very effective both for FLGPR and FLIR based EHD.
Publication Title
Recent Advances in Computational Intelligence in Defense and Security
Recommended Citation
Havens, T. C.,
Anderson, D.,
Stone, K.,
&
Pinar, A.
(2015).
Computational intelligence methods in forward-looking explosive hazard detection.
Recent Advances in Computational Intelligence in Defense and Security, 13-44.
http://doi.org/10.1007/978-3-319-26450-9_2
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1075