Results 171 to 180 of about 309,259 (214)

Improving the performance of sparse unmixing

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
Sparse unmixing has been proposed for hyperspectral image analysis. It has been shown that improved performance can be achieved when endmembers from a spectral library are used. However, when endmembers from image data have to be employed for unmixing, such a sparse-constrained approach may be problematic due to the fact that endmembers are generally ...
Qian Du 0001, Ben Ma, Nareenart Raksuntorn   +2 more
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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, José M. Bioucas-Dias, Antonio Plaza   +2 more
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A greedy algorithm for sparse unmixing

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Hyperspectral imaging sensors provide image data containing both spatial and detailed spectral information. However, due to low spatial resolution, the pixels in hyperspectral images are actually mixtures of the spectral signatures of the materials. Sparse unmixing assumes that these mixed pixels are sparse linear combinations of different material ...
Yuksel, Seniha Esen, TOKER, KEMAL GÜRKAN   +1 more
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Sparse unmixing with adaptive background

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
We propose a new hyperspectral sparse unmixing method under the assumption of the availability of a spectral library. Hyperspectral signals inevitably possess non-linearity or distortion caused by the presence of endmembers outside of the collection, inaccurate measurement of atmosphere, and endmember mismatches.
Yuki Itoh, Mario Parente
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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, José M. Bioucas-Dias   +3 more
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Sparse Distributed Multitemporal Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2017
Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral ima-ges (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, José M. Bioucas-Dias   +3 more
openaire   +1 more source

Robust Sparse Unmixing for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2018
A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library.
Dan Wang 0005, Zhenwei Shi 0001, Xinrui Cui   +2 more
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Sparse Unmixing of Hyperspectral Data: The Legacy of SUnSAL

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
In the last decade, the sparse regression approach was established as a new paradigm in hyperspectral unmixing. This paper reviews various directions in sparse unmixing, starting from the initial formulation proposed by Prof. Jose Bioucas-Dias: Sparse Unmixing via variable Splitting and Augmented Lagrangian (SUnSAL).
Mario Parente, Marian-Daniel Iordache
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Parallel sparse unmixing of hyperspectral data

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction ...
José M. Rodriguez Alves, José M. P. Nascimento, José M. Bioucas-Dias, Antonio Plaza, Vítor Silva 0001   +4 more
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Sparse filtering based hyperspectral unmixing

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
This work proposes a hyperspectral unmixing technique based on sparse filtering approach. The proposed method exploits the sparsity of feature distribution rather than modeling the data distribution. The proposed sparse filtering based unmixing procedure is essentially parameter-free, and the only parameter is to find the number of endmembers to be ...
Hemant Kumar Aggarwal, Angshul Majumdar
openaire   +2 more sources