GPU-Based Processing for Airborne Detection

Abstract

On-board real-time processing is highly desirable in airborne detection applications. as the data processing involved here is computationally expensive, typically high power multi-rack system is required to achieve real-time detection. Use of such hardware on-board is often not feasible in airborne applications due to space and power constraints. Recently, there has been a lot of interest in the use of Graphics Processing Units (GPUs) for real-time image processing because of their highly parallel architecture, low cost, and compact size. with the introduction of high level languages like C/CUDA (Nvidia), CTM (ATI), OpenCL, etc., GPUs are enjoying a manifold increase in their adoption for general purpose computation. in this paper we present GPU bound implementations of image registration and multiband RX anomaly detector. We identify the sub-problems, namely band-to-band registration, phase correlation, feature detection, feature tracking and image transformation, that can be efficiently parallelized on the SIMD architecture of the GPU. the results from experiments using these implementation are compared against existing implementation written in Matlab and C++. © 2010 Copyright SPIE - the International Society for Optical Engineering.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Airborne detection; band-to-band registration; CUDA; GP-GPU; image registration; RX anomaly detector

International Standard Book Number (ISBN)

978-081948128-3

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 Dec 2010

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