Experimental comparison of functional and multivariate spectral-based supervised classification methods in hyperspectral image
From MaRDI portal
Publication:5036392
DOI10.1080/02664763.2017.1414162OpenAlexW2780334501MaRDI QIDQ5036392
Anthony Zullo, Frédéric Ferraty, Mathieu Fauvel
Publication date: 23 February 2022
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2017.1414162
support vector machinesmixture modelsrandom forestfunctional multinomial logit modelhyperspectral profilenonparametric functional discrimination
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Image analysis in multivariate analysis (62H35) Applications of statistics in engineering and industry; control charts (62P30) Applications of statistics (62Pxx)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bagging predictors
- Kernel methods in machine learning
- Curves discrimination: a nonparametric functional approach
- Segmentation and classification of hyperspectral images using watershed transformation
- A constrained formulation of maximum-likelihood estimation for normal mixture distributions
- Applied functional data analysis. Methods and case studies
- Model-based clustering of high-dimensional data: a review
- Using \textsc{Bagidis} in nonparametric functional data analysis: predicting from curves with sharp local features
- Robust supervised classification with mixture models: learning from data with uncertain labels
- Generalized functional linear models
- High-dimensional generalized linear models and the lasso
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- Effect of dimensionality on discrimination
- 10.1162/153244302760185243
- Dimension Reduction: A Guided Tour
- Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data
- Kernel Methods for Remote Sensing Data Analysis
- Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data
- Generalized Linear Models with Functional Predictors
- A Bayesian approach to estimating linear mixtures with unknown covariance structure
- A non-stationary spatial generalized linear mixed model approach for studying plant diversity
- Bayesian scale space analysis of temporal changes in satellite images
- High-Dimensional Discriminant Analysis
- Functional Modelling and Classification of Longitudinal Data*
- Random forests