Results 161 to 170 of about 6,322 (191)

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, Jose M. Bioucas-Dias   +3 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

SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network

IEEE Geoscience and Remote Sensing Letters, 2022
In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing. It uses a deep convolutional encoder-decoder to generate the abundances relying on a spectral library.
Behnood Rasti, Bikram Koirala
openaire   +3 more sources

Framelet-Based Sparse Unmixing of Hyperspectral Images

IEEE Transactions on Image Processing, 2016
Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing.
Guixu, Zhang, Yingying, Xu, Faming, Fang
openaire   +2 more sources

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, Ben Ma, Nareenart Raksuntorn
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, Zhenwei Shi, Xinrui Cui
openaire   +1 more source

Hyperspectral Sparse Unmixing via Nonconvex Shrinkage Penalties

IEEE Transactions on Geoscience and Remote Sensing, 2023
Hyperspectral sparse unmixing aims at finding the optimal subset of spectral signatures in the given spectral library and estimating their proportions in each pixel. Recently, simultaneously sparse and low-rank representations (SSLRRs) have been widely used in the hyperspectral sparse unmixing task.
Ren, Longfei, Hong, Danfeng, Gao, Lianru, Sun, Xu, Huang, Min, Chanussot, Jocelyn   +5 more
openaire   +3 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

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, Jose M. Bioucas-Dias, Antonio Plaza   +2 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 ...
Alves, José M. Rodriguez, Nascimento, Jose, Bioucas-Dias, José M., Plaza, António, Silva, Vítor   +4 more
openaire   +2 more sources