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GPU Implementation of Iterative-Constrained Endmember Extraction from Remotely Sensed Hyperspectral Images

GPU Implementation of Iterative-Constrained Endmember Extraction from Remotely Sensed Hyperspectral Images


Titill: GPU Implementation of Iterative-Constrained Endmember Extraction from Remotely Sensed Hyperspectral Images
Höfundur: Plaza, Antonio   orcid.org/0000-0002-9613-1659
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Sigurdsson, Eysteinn Már
Útgáfa: 2015-06
Tungumál: Enska
Svið: Verkfræði- og náttúruvísindasvið
School of Engineering and Natural Sciences
Deild: Rafmagns- og tölvuverkfræðideild
Faculty of Electrical and Computer Engineering
ISSN: 1939-1404
DOI: 10.1109/JSTARS.2015.2441699
Efnisorð: Graphics processing units (GPUs); hyperspectral imaging; iterative constrained endmembers (ICEs); spectral unmixing
URI: https://hdl.handle.net/20.500.11815/40

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Tilvitnun:

Sigurdsson, Einar Már ; Plaza, Antonio ; Benediktsson, Jón Atli GPU Implementation of Iterative-Constrained Endmember Extraction from Remotely Sensed Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, pp. 2939-2949, 2015.

Útdráttur:

Hyperspectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. Linear spectral unmixing is frequently used to characterize mixed pixels in hyperspectral data. Over the last few years, many techniques have been proposed for identifying pure spectral signatures (endmembers) in hyperspectral images. The iterated constrained endmembers (ICE) algorithm is an iterative method that uses the linear model to extract endmembers and abundances simultaneously from the data set. This approach does not necessarily require the presence of pixels in the hyperspectral image as it can automatically derive the signatures of endmembers even if these signatures are not present in the data. As it is the case with other endmember identification algorithms, ICE suffers from high computational complexity. In this paper, a complete and scalable adaptation of the ICE algorithm is implemented using the parallel nature of commodity graphics processing units (GPUs). This gives significant speed increase over the traditional ICE method and allows for processing of larger data set with an increased number of endmembers.

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