Results 161 to 170 of about 6,103 (201)

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
null Junmin Liu, null Jiangshe Zhang
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Robust Context Dependent Spectral Unmixing

2014 22nd International Conference on Pattern Recognition, 2014
A robust hyper spectral unmixing algorithm that finds multiple sets of end members is introduced. The algorithm, called Robust Context Dependent Spectral Unmixing (RCDSU), combines the advantages of context dependent unmixing and robust clustering. RCDSU adapts the unmixing to different regions, or contexts, of the spectral space. It combines fuzzy and
Hamdi Jenzri, Hichem Frigui, Paul Gader
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Variational methods for spectral unmixing of hyperspectral unmixing

2011
International ...
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Snoussi, Hichem   +3 more
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Spectral Unmixing Technique of HSI

2015
Relative to the classification technique, the spectral unmixing (Keshava and Mustard in IEEE Trans Sig Process Mag 19:44–57, 2002) i.e., soft classification technique started late. Although the spectral resolution of the hyperspectral image has been improved greatly, the spatial resolution of the corresponding land object target of the pixel has been ...
Liguo Wang, Chunhui Zhao
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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.
Bjorn Labitzke, Andreas Kolb
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Spectral unmixing using nonnegative tensor factorization

Proceedings of the 45th annual southeast regional conference, 2007
Three major objectives in processing hyperspectral image data of an object (target) are data compression, spectral signature identification of constituent materials, and determination of their corresponding fractional abundances. Here we propose a novel approach to processing hyperspectral data using nonnegative tensor factorization (NTF), which ...
Qiang Zhang, Han Wang, Robert Plemmons, V. Paul Pauca   +3 more
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Spectral unmixing with nonnegative matrix factorization

SPIE Proceedings, 2006
The present study is an illustration of the application of Nonnegative Matrix Factorization (NMF) to the problem of linear unmixing of mineral endmembers in hyperspectral images. NMF can be seen as for nonnegative linear coding of the data points.
Mario Parente, Argyrios Zymnis, Joëlle Skaf, Janice Bishop   +3 more
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Spectral unmixing of Landsat Thematic Mapper data

International Journal of Remote Sensing, 1995
The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel. Despite this knowledge most image classification techniques aim at labelling a pixel according to a singular surface category.
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Spectral unmixing

IEEE Signal Processing Magazine, 2002
N. Keshava, J.F. Mustard
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Local spectral unmixing for target detection

2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2016
Remotely sensed hyperspectral imagery provides, at each pixel, a radiance spectrum with up to hundreds of distinct wavelength channels. This high-dimensional spectral information allows for pixel-level material discrimination, including applications to remotely detecting the presence of particular materials of interest within a scene.
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