Results 151 to 160 of about 1,511 (182)

Deblurring and Sparse Unmixing for Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2013
The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed ...
Xi-Le Zhao, Fan Wang, Ting-Zhu Huang, Michael K. Ng 0001, Robert J. Plemmons   +4 more
openaire   +1 more source

A Dataset with Ground-Truth for Hyperspectral Unmixing

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Spectral unmixing is one of the most important issues of hyperspectral data processing. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms. In this work, we create several experimental scenes in our laboratory with controlled settings where the pure ...
Min Zhao 0014, Jie Chen 0022
openaire   +1 more source

Random Hadamard Projections for Hyperspectral Unmixing

IEEE Geoscience and Remote Sensing Letters, 2017
Dimensionality reduction based on random projections is investigated in the context of spectral unmixing of hyperspectral imagery with aims toward unmixing accuracy and computational efficiency. To this end, both Hadamard-based random projections—which significantly reduce computational costs with respect to more traditional Gaussian-driven projections—
Vineetha Menon, Qian Du 0001, James E. Fowler   +2 more
openaire   +1 more source

Sparse and low rank hyperspectral unmixing

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
openaire   +1 more source

Collaborative sparse unmixing of hyperspectral data

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in a large collection of pure spectral signatures (spectral library), known a priori.
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
openaire   +1 more source

Joint denoising and unmixing for hyperspectral image

2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014
Hyperspectral image denoising and unmixing are two separate stages in traditional works. Unmixing algorithm is implemented after denoising. The performance of unmixing will be promoted if noise in hyperspectral image is removed well. But the result of unmixing can not be used to improve the result of denoising.
Yongqiang Zhao 0001, Jingxiang Yang, Chen Yi, Yong Liu   +3 more
openaire   +1 more source

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
openaire   +2 more sources

Variational methods for spectral unmixing of hyperspectral unmixing

2011
International ...
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Snoussi, Hichem   +3 more
openaire   +2 more sources

FPGA-based architecture for hyperspectral unmixing

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
This paper proposes an FPGA-based architecture for onboard hyperspectral unmixing. This method based on the Vertex Component Analysis (VCA) has several advantages, namely it is unsupervised, fully automatic, and it works without dimensionality reduction (DR) pre-processing step. The architecture has been designed for a low cost Xilinx Zynq board with a
Nascimento, Jose, Véstias, Mário, Martin, Gabriel   +2 more
openaire   +2 more sources

Reweighted Sparse Regression for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2016
Hyperspectral unmixing (HSU) plays an important role in hyperspectral image (HSI) analysis. Recently, the HSU method based on sparse regression has drawn much attention. This paper presents a new weighted sparse regression problem for HSU and proposes two iterative reweighted algorithms for solving this problem, where the weights used for the next ...
Cheng Yong Zheng, Hong Li 0009, Qiong Wang, C. L. Philip Chen   +3 more
openaire   +1 more source