Results 161 to 170 of about 9,243 (189)

Parallel Hyperspectral Unmixing on GPUs

IEEE Geoscience and Remote Sensing Letters, 2014
This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions.
Nascimento, Jose, Bioucas-Dias, José M., Alves, José M. Rodriguez, Silva, Vítor, Plaza, António   +4 more
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Compressed sensing based hyperspectral unmixing

2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014
In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of endmember spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based convex optimization techniques or compressive sensing can be used for hyperspectral unmixing.
Gürbüz, Ali Cafer, Gunyel, Bertan, Albayrak, R. Tufan   +2 more
openaire   +2 more sources

Sparse distributed hyperspectral unmixing

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, Jose M. Bioucas-Dias   +3 more
openaire   +1 more source

Semi-supervised hyperspectral unmixing

2014 IEEE Geoscience and Remote Sensing Symposium, 2014
In this paper, an effective method is proposed that combines supervised and unsupervised unmixing. We assume a linear model for the hyperspectral data and incorporate information about endmembers that are known to be in the data into the model. This information can be acquired from a spectral library or extracted from the data.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
openaire   +1 more source

Fast multitemporal hyperspectral unmixing

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
In this paper, we present a fast blind multitemporal hyperspectral unmixing algorithm, using an l 1 penalty to promote sparse abundances. The method is able to account for different acquisition conditions of multitemporal images, by allowing the spectral signatures in the different temporal images to vary. The new algorithm is tested on simulated data
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
openaire   +1 more source

Unmixing hyperspectral intimate mixtures

SPIE Proceedings, 2010
This paper addresses the unmixing of hyperspectral images, when intimate mixtures are present. In these scenarios the light suffers multiple interactions among distinct endmembers, which is not accounted for by the linear mixing model. A two-step method to unmix hyperspectral intimate mixtures is proposed: first, based on the Hapke intimate mixture
Nascimento, Jose, Bioucas-Dias, José M.
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Unmixing sparse hyperspectral mixtures

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the
Marian-Daniel Iordache, Jose Bioucas-Dias, Antonio Plaza   +2 more
openaire   +1 more source

Spatially Adaptive Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2011
Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing ...
Kelly Canham, Ariel Schlamm, Amanda Ziemann, Bill Basener, David Messinger   +4 more
openaire   +1 more source

A Sturdy Nonlinear Hyperspectral Unmixing

IETE Journal of Research, 2020
Hyperspectral unmixing (HSU) is a way to process the prediction of the existing endmembers and the fractional abundances (FA) available in all pixels in the hyperspectral images.
M. Venkata Sireesha, P. V. Naganjaneyulu, K. Babulu   +2 more
openaire   +1 more source

Segmentation-based cNMF for hyperspectral unmixing

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
This paper presents a modification to the cNMF for unmixing where the image is first segmented and the cNMF is applied to individual segments for endmember extraction. Extracted spectral endmembers from individual segments are clustered in endmember classes to describe the entire image. The approach is compared with the global cNMF.
Alkhatib, Mohammed Q., Velez-Reyes, Miguel   +1 more
openaire   +1 more source