Results 141 to 150 of about 1,157 (179)

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

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
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

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
openaire   +1 more source

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   +1 more source

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
openaire   +1 more source

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 Dictionary Learning for Blind Hyperspectral Unmixing

IEEE Geoscience and Remote Sensing Letters, 2019
Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity.
Yang Liu, Yi Guo 0001, Feng Li 0003, Lei Xin, Puming Huang   +4 more
openaire   +2 more sources