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An analysis of the nonlinear spectral mixing of didymium and soda-lime glass beads using hyperspectral imagery (HSI) microscopy

Ronald G. Resmini, Robert S. Rand, David W. Allen, Christopher J. Deloye, Miguel Velez-Reyes, Fred A. Kruse
2014 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX  
Analysis methods are linear spectral unmixing (LSU), LSU applied to reflectance converted to single-scattering albedo (SSA) using Hapke theory, and two kernel-based methods.  ...  These aspects of spectral mixture analysis theory are well documented. Several methods to invert (and model) nonlinear spectral mixtures have been proposed.  ...  Analysis methods applied to the reflectance data are linear spectral unmixing (LSU); LSU applied to reflectance converted to single-scattering albedo (SSA) using Hapke theory; and two kernel-based methods  ... 
doi:10.1117/12.2051434 fatcat:zd67yuabc5dwzafcsbfbui22ey

Nonlinear Estimation of Hyperspectral Mixture Pixel Proportion Based on Kernel Orthogonal Subspace Projection [chapter]

Bo Wu, Liangpei Zhang, Pingxiang Li, Jinmu Zhang
2006 Lecture Notes in Computer Science  
The algorithm applies linear regressive model to the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared-error regressor.  ...  A kernel orthogonal subspace projection (KOSP) algorithm has been developed for nonlinear approximating subpixel proportion in this paper.  ...  Conclusions This paper has presented a kernel based least square mixture model which makes use of the nonlinear characteristics of spectral mixture to obtain the higher unmixing accuracies.  ... 
doi:10.1007/11759966_157 fatcat:zq6p6d44wjamrbvkyrolckumni

Hyperspectral Data Processing Algorithms [chapter]

Antonio Plaza, Javier Plaza, Gabriel Martín, Sergio Sánchez
2018 Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation  
mixture analysis [4] .  ...  Mostly based on previous efforts in multispectral imaging, full-pixel techniques assume that each pixel vector measures the response of one single underlying material.  ...  A standard technique for spectral mixture analysis is linear spectral unmixing [19] , which assumes that the collected spectra at the spectrometer can be expressed in the form of a linear combination  ... 
doi:10.1201/9781315164151-11 fatcat:gm3twsueujh3jla6wzuo2piamq

Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

Jie Chen, Cédric Richard, Paul Honeine
2013 IEEE Transactions on Signal Processing  
In this paper, we formulate a new kernel-based paradigm that relies on the assumption that the mixing mechanism can be described by a linear mixture of endmember spectra, with additive nonlinear fluctuations  ...  Index Terms-Hyperspectral imaging, multi-kernel learning, nonlinear spectral unmixing, support vector regression.  ...  The analysis in Section III-B has shown strong connections between our kernel-based model and well-characterized models, e.g., the generalized bilinear mixture model.  ... 
doi:10.1109/tsp.2012.2222390 fatcat:m35fa2xisrcm5o42zhwm4bk7j4

Kernel based sparse NMF algorithm for hyperspectral unmixing

Wenhong Wang, Yuntao Qian
2016 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
In this paper, we re gard the spectral unmixing as a blind source separation prob lem in the feature space, and develop a novel kernel based nonnegative matrix factorization method to estimate the end  ...  Nonlinear unmixing methods for hyperspectral image (HSI) have attracted increasing interests since they can overcome the inherent limitations of the linear ones.  ...  The popular used model for the spectral mixture of H SI is the linear mixing model (LMM).  ... 
doi:10.1109/igarss.2016.7730818 dblp:conf/igarss/WangQ16a fatcat:m4tzw3mpvfbnxgq2xrxpag6vbi

Nonlinear estimation of subpixel proportion via kernel least square regression

L. Zhang, B. Wu, B. Huang, P. Li
2007 International Journal of Remote Sensing  
Spectral mixture analysis is an efficient approach to spectral decomposition of hyperspectral remotely sensed imagery, using land cover proportions which can be estimated from pixel values through model  ...  This algorithm was also compared with other widely used kernel based algorithms, i.e. support vector regression and radial basis function neutral network and the results show that the proposed algorithm  ...  Conclusions This paper has presented a kernel-based least square mixture model making use of the nonlinear characteristics of spectral mixture to obtain the higher unmixing accuracies.  ... 
doi:10.1080/01431160600993454 fatcat:pwhu4z77rnhalclz36w6drxj4u

A novel kernel-based nonlinear unmixing scheme of hyperspectral images

Jie Chen, Cedric Richard, Paul Honeine
2011 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)  
In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory.  ...  In hyperspectral images, pixels are mixtures of spectral components associated to pure materials.  ...  Note that the measures must be non-negative and sum-to-one similarly to the constraints in linear mixing model. III. KERNEL BASED NONLINEAR ANALYSIS OF HYPERSPECTRAL IMAGES A.  ... 
doi:10.1109/acssc.2011.6190353 dblp:conf/acssc/ChenRH11 fatcat:3c5prig2yzfr3gwxyppmde4spa

A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION

Linmei Wu, Li Shen, Zhipeng Li
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based  ...  In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic  ...  Figure 5 , 5 (a) is the result to the original kernel method, (b) comes from the spectral-spatial-based kernel method, (c) is the classification map of spectral-structure-based kernel approach, and (d)  ... 
doi:10.5194/isprsarchives-xli-b7-399-2016 fatcat:w3sec23hangalff33giqhk6if4

A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION

Linmei Wu, Li Shen, Zhipeng Li
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based  ...  In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic  ...  Figure 5 , 5 (a) is the result to the original kernel method, (b) comes from the spectral-spatial-based kernel method, (c) is the classification map of spectral-structure-based kernel approach, and (d)  ... 
doi:10.5194/isprs-archives-xli-b7-399-2016 fatcat:3zxlscbt7vd45pruah62bqzp6i

Near infrared hyperspectral imaging of blends of conventional and waxy hard wheats

Stephen Delwiche, Jianwei Qin, Robert Graybosch, Steven Rausch, Moon Kim
2018 Journal of Spectral Imaging  
Alternatively, hyperspectral imaging (HSI) offers the ability to develop spectrally based models at the individual kernel level, while doing so on all kernels within the hypercube image.  ...  Spectral analysis Each identified object from image processing, hereafter termed a kernel, was reduced to a mean reflectance spectrum by averaging over all pixels within the object.  ... 
doi:10.1255/jsi.2018.a2 fatcat:4e3d2ap64jfhxesi75yan3jery

Characterizing intimate mixtures of materials in hyperspectral imagery with albedo-based and kernel-based approaches

Robert S. Rand, Ronald G. Resmini, David W. Allen, Thomas S. Pagano, John F. Silny
2015 Imaging Spectrometry XX  
The first method is based on earlier studies that indicate non-linear mixtures in reflectance space are approximately linear in albedo space.  ...  The second method is motivated by the same idea, but uses a kernel that seeks to capture the linear behavior of albedo in nonlinear mixtures of materials.  ...  of the KFCLS method has resulted in (1) the development of a generalized kernel for areal (linear) and intimate (non-linear) mixtures 15 and (2) an adaptive kernel-based technique for mapping areal  ... 
doi:10.1117/12.2190067 fatcat:kihs7nr3frbgnlgtudrwnl2wbq

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China

Cuicui Ji, Yonghong Jia, Zhihai Gao, Huaidong Wei, Xiaosong Li, Daniel Capella Zanotta
2017 PLoS ONE  
Spectral Mixture Analysis (SMA) is a widely used technique to retrieve f pv and f npv [7-9], which is preferred over traditional vegetation index methods.  ...  ., Linear Spectral Mixture Model, LSMM) model a mixed spectrum as a linear combination of pure spectral signatures of its constituent components, weighted by their sub-pixel fractional cover [10] .  ...  Kernel-based nonlinear spectral mixture model. Kernel method.  ... 
doi:10.1371/journal.pone.0189292 pmid:29240777 pmcid:PMC5730155 fatcat:eh7thbhxozca5mihypna4f22qm

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

Akpona Okujeni, Sebastian van der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, Patrick Hostert
2014 Remote Sensing  
SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving  ...  Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer  ...  In contrast, MESMA uses an iterative procedure with multiple linear mixture models for mapping all land cover categories considered in the analysis.  ... 
doi:10.3390/rs6076324 fatcat:j7skplewsrc4beycmyeh5pyema

Spatial/spectral endmember extraction by multidimensional morphological operations

A. Plaza, P. Martinez, R. Perez, J. Plaza
2002 IEEE Transactions on Geoscience and Remote Sensing  
Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery.  ...  Index Terms-Automated endmember extraction, mathematical morphology, morphological eccentricity index, multidimensional analysis, spatial/spectral integration, spectral mixture model.  ...  used to describe several nonpure pixels through a mixture model based on linear combinations of endmembers [11] .  ... 
doi:10.1109/tgrs.2002.802494 fatcat:7p7p3uezo5caleyatp5rbd5ogy

Compositional Inductive Biases in Function Learning [article]

Eric Schulz, Joshua B. Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
2016 bioRxiv   pre-print
We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches.  ...  Spectral mixture kernel The second approach is based on the fact that any stationary kernel 7 can be expressed as an integral using Bochner's theorem.  ...  We computed this approximation for every sample that a participant saw under different kernel choices: linear, RBF, spectral mixture, or a compositional kernel.  ... 
doi:10.1101/091298 fatcat:gn3p2z23r5a4xft66xklxp5eou
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