DocumentCode :
3691122
Title :
GPU implementation of spatial preprocessing for spectral unmixing of hyperspectral data
Author :
Jaime Delgado;Gabriel Martin;Javier Plaza;Luis Ignacio Jimenez;Antonio Plaza
Author_Institution :
Hyperspectral Computing Laboratory, University of Extremadura, Caceres, Spain
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
5043
Lastpage :
5046
Abstract :
The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmem-bers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidiaTMGeForce GTX 580 and NVidiaTMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.
Keywords :
"Graphics processing units","Hyperspectral imaging","Kernel","Instruction sets","Computer architecture"
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International
ISSN :
2153-6996
Electronic_ISBN :
2153-7003
Type :
conf
DOI :
10.1109/IGARSS.2015.7326966
Filename :
7326966
Link To Document :
بازگشت