Results 181 to 190 of about 309,259 (214)

Sparse unmixing of hyperspectral data with bandwise model

Information Sciences, 2020
Abstract Sparse unmixing has long been a hot research topic in the area of hyperspectral image (HSI) analysis. Most of the traditional sparse unmixing methods usually assume to only take the Gaussian noise into consideration. However, there are also other types of noise in real HSI, i.e., impulse noise, stripes, dead lines and so on. In addition, the
Rencheng Song, Chang Li, Chenhong Sui
exaly   +2 more sources

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

Nonlocal Tensor-Based Sparse Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2021
Sparse unmixing is an important technique for analyzing and processing hyperspectral images (HSIs). Simultaneously exploiting spatial correlation and sparsity improves substantially abundance estimation accuracy. In this article, we propose to exploit nonlocal spatial information in the HSI for the sparse unmixing problem.
Jie Huang 0005, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng   +3 more
openaire   +2 more sources

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   +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

Robust sparse unmixing of hyperspectral data

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Sparse unmixing (SU) of hyperspectral data has recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (
Yong Ma 0001, Chang Li 0001, Jiayi Ma 0001   +2 more
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Multiobjective sparse unmixing approach with noise removal

Proceedings of the Genetic and Evolutionary Computation Conference, 2018
In sparse hyperspectral unmixing, regularization methods inevitably suffer from the "decision ahead of solution" issue concerning the regularization parameter, which is not conducive to practical applications. To settle this issue, a two-phase multiobjective sparse unmixing (Tp-MoSU) approach has been proposed recently.
Xiangming Jiang, Maoguo Gong, Tao Zhan 0005, Zedong Tang   +3 more
openaire   +2 more sources

Complete dictionary online learning for sparse unmixing

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Sparse unmixing has been successfully applied to hyperspectral remote sensing imagery, based on an available standard spectral library. However, as the number of hyperspectral remote sensors increases, more and more hyperspectral remote sensing images are requiring analysis without the use of a corresponding standard spectral library.
Ruyi Feng, Yanfei Zhong, Liangpei Zhang 0001   +2 more
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Total variation regulatization in sparse hyperspectral unmixing

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011
Hyperspectral unmixing has recently been addressed as a sparse regression problem by using predefined spectral libraries instead of image-derived endmembers in the unmixing process. This new approach has attracted much attention, as it sidesteps well known obstacles met in endmember extraction, such as the stopping criteria for the extraction process ...
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio J. Plaza   +2 more
openaire   +1 more source

On the performance of sparse unmixing on non-linear mixtures

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
This paper explores the performance of sparse unmixing (SU) on non-linear mixtures. We consider SU as an endmember selection method from a spectral library and measure its performance using recently proposed approximately perfect recovery condition for sparse unmixing, comparing with non-negative least squares (NNLS). We also further explore the effect
Yuki Itoh, Mario Parente
openaire   +1 more source