Results 31 to 40 of about 871,644 (244)
Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection [PDF]
, 2014 This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional ...
Altmann, Yoann +10 more
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Nonlinear unmixing of minerals based on the log and continuum removal model
European Journal of Remote Sensing, 2019 Spectral mixing models for minerals can be complex, and choosing the right unmixing model is indispensable to ensure the accuracy of spectral unmixing. Continuum removal (CR) and natural log operation have the potential to eliminate nonlinear effects in ...
Hengqian Zhao, Xuesheng Zhao
doaj +1 more source
Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image.
Fan Li +5 more
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Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]
, 2013 When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
McLaughlin, Stephen; id_orcid +14 more
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A Probabilistic Approach to Spectral Unmixing [PDF]
, 2010 In this paper, we present a statistical approach to spectral unmixing with unknown endmember spectra and unknown illuminant power spectrum. The method presented here is quite general in nature, being applicable to settings in which sub-pixel information is required.
Huynh, Cong, Robles-Kelly, Antonio
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Robust spectral unmixing for anomaly detection [PDF]
2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014 This paper is concerned with a joint Bayesian formulation for determining the endmembers and abundances of hyperspectral images along with sparse outliers which can lead to estimation errors unless accounted for. We present an inference method that generalizes previous work and provides a MCMC estimate of the posterior distribution. The proposed method
Gregory E. Newstadt +2 more
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Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]
, 2008 This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive ...
Chang, Chein-I +5 more
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Context Dependent Spectral Unmixing
2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012 A hyperspectral unmixing algorithm that finds multiple sets of endmembers is introduced. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel objective function that combines context identification and unmixing into a joint ...
Hamdi Jenzri +2 more
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Hyperspectral image unmixing using a multiresolution sticky HDP [PDF]
, 2012 This paper is concerned with joint Bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors.We propose a generative model for hyperspectral images in which the abundances are sampled from a ...
Hero, Alfred O. +3 more
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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Eches, Olivier +3 more
core +1 more source