Results 151 to 160 of about 6,533 (191)

Spectral unmixing

IEEE Signal Processing Magazine, 2002
Nirmal Keshava, John F. Mustard
exaly   +2 more sources

Spectral unmixing

International Journal of Remote Sensing, 2012
Satellite imagery is formed by finite digital numbers representing a specific location of ground surface in which each matrix element is denominated as a picture element or pixel. The pixels represent the sensor measurements of spectral radiance. The radiance recorded in the satellite images is then an integrated sum of the radiances of all targets ...
Carmen Quintano, Alfonso Fernández-Manso, Yosio E. Shimabukuro, Gabriel Pereira   +3 more
openaire   +1 more source

Optimal linear spectral unmixing

IEEE Transactions on Geoscience and Remote Sensing, 1999
The optimal estimate of ground cover components of a linearly mixed spectral pixel in remote-sensing imagery is investigated. The problem is formulated as two consecutive constrained least-squares (LS) problems: the first problem concerns the estimation of the end-member spectra (EMS), and the second concerns the estimate, within each mixed pixel, of ...
Yu Hen Hu, H. B. Lee, F. L. Scarpace
openaire   +1 more source

Spectral Unmixing Using Autoencoder with Spatial and Spectral Regularizations

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
In this paper, we propose a novel approach for spectral unmixing based on unsupervised learning using autoencoder with Inhomogeneous Gaussian Markov random field (IGMRF) as prior for regularization. The decoder part of our autoencoder has linear weights making it a linear mixture model (LMM).
Jignesh R. Patel, Manjunath V. Joshi, Jignesh S. Bhatt   +2 more
openaire   +1 more source

Spatially informed spectral unmixing

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015
Spectral unmixing methods have traditionally relied on the plethora of information in the spectral domain to resolve subpixel components. Several new methods have utilised the spatial information contained within the hyperspectral image, however these are limited to the spatial resolution of the sensor.
Daniel L. Bongiorno, Adam J. Fairley, Stefan B. Williams   +2 more
openaire   +1 more source

Spatial–spectral preprocessing for spectral unmixing

International Journal of Remote Sensing, 2018
Most techniques available in the endmember extraction rely on exploiting the spectral information of the data alone.
Yang Yan, Wenshen Hua, Xun Liu, Zihao Cui, Dongmei Diao   +4 more
openaire   +1 more source

Spectral unmixing using distance geometry

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011
In this paper, we present a new method for solving the spectral unmixing problem which uses only the spectral distances between the data points and the endmembers. This method is obtained by reformulating every step of the recently developed SPU algorithm entirely in distance geometry, yielding a recursive algorithm based on the geometrical properties ...
Rob Heylen, Paul Scheunders
openaire   +2 more sources

Efficient and accurate linear spectral unmixing

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
The techniques of multi- and hyperspectral imaging have gained a growing attention in recent years. This is mostly due to their potential to provide rich information that can be used to improve material classification or product quality assessment. Linear spectral unmixing is a standard approach in hyperspectral data analysis.
Björn Labitzke, Andreas Kolb 0001
openaire   +1 more source

Iterative spectral unmixing (ISU)

International Journal of Remote Sensing, 1999
Spectral unmixing techniques strive to find proportions of end-members within a pixel from the observed mixed pixel spectrum and a number of pure end-member spectra of known composition. The outcomes of such analysis are fraction (abundance) images for the selected (pure) end-members and a root mean square (RMS) error estimate representing the ...
openaire   +2 more sources

Spectral Unmixing via Compressive Sensing

IEEE Transactions on Geoscience and Remote Sensing, 2014
The recently developed theory of compressive sensing (CS) exhibits enormous potentials in signal recovery. In this paper, we investigate its application on spectral unmixing, which appears in hyperspectral data analysis and is usually based on a linear mixture model (LMM) that assumes that a mixed pixel is a linear combination of a set of pure spectral
Junmin Liu, Jiang-She Zhang 0001
openaire   +1 more source