NDLI: Real-Time Identification of Hyperspectral Subspaces

Content Provider	IEEE Xplore Digital Library
Author	Torti, E. Acquistapace, M. Danese, G. Leporati, F. Plaza, A.
Copyright Year	2008
Abstract	The identification of signal subspace is a crucial operation in hyperspectral imagery, enabling a correct dimensionality reduction that often yields gains in algorithm performance and efficiency. This paper presents new parallel implementations of a widely used hyperspectral subspace identification with minimum error (HySime) algorithm on different types of high-performance computing architectures, including general purpose multicore CPUs, graphics processing units (GPUs), and digital signal processors (DSPs). We first developed an optimized serial version of the HySime algorithm using the C programming language, and then we developed three parallel versions: one for a multi-core Intel CPU using the OpenMP API and the ATLAS algebra library, another one using NVIDIA's compute unified device architecture (CUDA) and its basic linear algebra subroutines library (CuBLAS), and another one using a Texas' multicore DSP. Experimental results, based on the processing of simulated and real hyperspectral images of various sizes, show the effectiveness of our GPU and multicore CPU implementations, which satisfy the real-time constraints given by the data acquisition rate. The DSP implementation offers a good tradeoff between low power consumption and computational performance, but it is still penalized by the absence of double precision floating point accuracy and/or suitable mathematical libraries.
Starting Page	2680
Ending Page	2687
Page Count	8
File Size	1336224
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 Hyperspectral imaging Digital signal processing Vectors Noise Multicore processing hyperspectral signal identification with minimum error (HySime) Digital signal processors (DSPs) 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

A Hybrid CPU–GPU Real-Time Hyperspectral Unmixing Chain

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

Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing

Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Parallel implementation of a hyperspectral unmixing chain: Graphic processing units versus multi-core processors

Fast Spatial Preprocessing for Spectral Unmixing of Hyperspectral Data on Graphics Processing Units

Assessing the Performance-Energy Balance of Graphics Processors for Spectral Unmixing

Parallel Implementation of Sparse Representation Classifiers for Hyperspectral Imagery on GPUs

A New Digital Repository for Hyperspectral Imagery With Unmixing-Based Retrieval Functionality Implemented on GPUs

Real-Time Identification of Hyperspectral Subspaces

Similar Documents

A Hybrid CPU–GPU Real-Time Hyperspectral Unmixing Chain

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

Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing

Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Parallel implementation of a hyperspectral unmixing chain: Graphic processing units versus multi-core processors

Fast Spatial Preprocessing for Spectral Unmixing of Hyperspectral Data on Graphics Processing Units

Assessing the Performance-Energy Balance of Graphics Processors for Spectral Unmixing

Parallel Implementation of Sparse Representation Classifiers for Hyperspectral Imagery on GPUs

A New Digital Repository for Hyperspectral Imagery With Unmixing-Based Retrieval Functionality Implemented on GPUs

Real-Time Identification of Hyperspectral Subspaces