Special Section on High-Performance Computing in Applied Remote Sensing: Part 2

Anomaly detection based on a parallel kernel RX algorithm for multicore platforms

[+] Author Affiliations
José M. Molero, Ester M. Garzón

University of Almería, Department of Computer Architecture and Electronics, Agrifood Campus of International Excellence (CEIA3), Ctra Sacramento s/n. 04120 Almería, Spain

Inmaculada García

University of Málaga, Department of Computer Architecture, Escuela de Ingenierías, Campus de Teatinos, 29071 Málaga, Spain

Antonio Plaza

University of Extremadura, Hyperspectral Computing Laboratory, Avda. de la Universidad s/n, E-10071 Cáceres, Spain

J. Appl. Remote Sens. 6(1), 061503 (May 10, 2012). doi:10.1117/1.JRS.6.061503
History: Received September 30, 2011; Revised February 15, 2012; Accepted March 7, 2012
Text Size: A A A

Abstract.  Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Yu, also called RX algorithm. It implements the Mahalanobis distance, which has been widely used in hyperspectral imaging applications. A variation of this algorithm, known as kernel RX (KRX), consists of applying the same concept to a sliding window centered around each image pixel. KRX is computationally very expensive because, for every image pixel, a covariance matrix and its inverse has to be calculated. We develop an efficient implementation of the kernel RX algorithm. Our proposed approach makes use of linear algebra libraries and further develops a parallel implementation optimized for multi-core platforms, which is a well known, inexpensive and widely available high performance computing technology. Experimental results for two hyperspectral data sets are provided. The first one was collected by NASA’s airborne visible infra-red imaging spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and the second one was collected by the hyperspectral digital image collection experiment (HYDICE). Our anomaly detection accuracy, evaluated using receiver operating characteristics (ROC) curves, indicates that KRX can significantly outperform the classic RX while achieving close to linear speedup in state-of-the-art multi-core platforms.

Figures in this Article
© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

José M. Molero ; Ester M. Garzón ; Inmaculada García and Antonio Plaza
"Anomaly detection based on a parallel kernel RX algorithm for multicore platforms", J. Appl. Remote Sens. 6(1), 061503 (May 10, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.061503


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.