NDLI: Efficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processors

Content Provider	IEEE Xplore Digital Library
Author	Molero, J.M. Garzon, E.M. Garcia, I. Quintana-Orti, E.S. Plaza, A.
Copyright Year	2008
Abstract	Anomaly detection is an important task for hyperspectral data exploitation. Although many algorithms have been developed for this purpose in recent years, due to the large dimensionality of hyperspectral image data, fast anomaly detection remains a challenging task. In this work, we exploit the computational power of commodity graphics processing units (GPUs) and multicore processors to obtain implementations of a well-known anomaly detection algorithm developed by Reed and Xiaoli (RX algorithm), and a local (LRX) variant, which basically consists in applying the same concept to a local sliding window centered around each image pixel. LRX has been shown to be more accurate to detect small anomalies but computationally more expensive than RX. Our interest is focused on improving the computational aspects, not only through efficient parallel implementations, but also by analyzing the mathematical issues of the method and adopting computationally inexpensive solvers. Futhermore, we also assess the energy consumption of the newly developed parallel implementations, which is very important in practice. Our optimizations (based on software and hardware techniques) result in a significant reduction of execution time and energy consumption, which are keys to increase the practical interest of the considered algorithms. Indeed, for RX, the runtime obtained is less than the data acquisition time when real hyperspectral images are used. Our experimental results also indicate that the proposed optimizations and the parallelization techniques can significantly improve the general performance of the RX and LRX algorithms while retaining their anomaly detection accuracy.
Starting Page	2256
Ending Page	2266
Page Count	11
File Size	1103030
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing units Correlation Hyperspectral imaging Multicore processing Optimization multicore processors Anomaly detection energy consumption graphics processing units (GPUs) hyperspectral imaging
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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