An MRF Model Based Approach for Sub-pixel Mapping from Hyperspectral Data

2004 ◽  
pp. 257-277 ◽  
Author(s):  
Teerasit Kasetkasem ◽  
Manoj K. Arora ◽  
Pramod K. Varshney
Keyword(s):  
Hyperspectral Data ◽  
Model Based ◽  
Pixel Mapping

Unsupervised classification of hyperspectral data: an ICA mixture model based approach

2004 ◽  
Vol 25 (2) ◽  
pp. 481-487 ◽  
Author(s):  
Chintan A. Shah ◽  
Manoj K. Arora ◽  
Pramod K. Varshney
Keyword(s):  
Mixture Model ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Model Based

Empirical versus Model‐based Atmospheric Correction of Digital Airborne Imaging Spectrometer Hyperspectral Data

2005 ◽  
Vol 20 (4) ◽  
pp. 21-28 ◽  
Author(s):  
Brian C. Hadley ◽  
Maria Garcia-Quijano ◽  
John R. Jensen ◽  
Jason A. Tullis
Keyword(s):  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Imaging Spectrometer ◽  
Model Based ◽  
Versus Model ◽  
Airborne Imaging

scholarly journals Sub-Pixel Mapping Model Based on Total Variation Regularization and Learned Spatial Dictionary

2021 ◽  
Vol 13 (2) ◽  
pp. 190
Author(s):  
Bouthayna Msellmi ◽  
Daniele Picone ◽  
Zouhaier Ben Rabah ◽  
Mauro Dalla Mura ◽  
Imed Riadh Farah
Keyword(s):  
Total Variation ◽  
Dictionary Learning ◽  
Regularization Parameter ◽  
Spatial Localization ◽  
Hyperspectral Data ◽  
Total Variation Regularization ◽  
Data Set ◽  
Mixed Pixel ◽  
Pixel Mapping ◽  
Spatial Regularization

In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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