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Effect of Denoising in Band Selection for Regression Tasks in Hyperspectral Datasets

  • Dades identificatives

    Identificador: imarina:9299005
    Autors:
    Latorre-Carmona, PedroMartinez Sotoca, JosePla, FilibertoBioucas-Dias, JoseJulia Ferre, Carme
    Resum:
    This paper presents a comparative analysis of six band selection methods applied to hyperspectral datasets for biophysical variable estimation problems, where the effect of denoising on band selection performance has also been analyzed. In particular, we consider four hyperspectral datasets and three regressors of different nature (epsilon-SVR, Regression Trees, and Kernel Ridge Regression). Results show that the denoising approach improves the band selection quality of all the tested methods. We show that noise filtering is more beneficial for the selection methods that use an estimator based on the whole dataset for the prediction of the output than for methods that use strategies based on local information (neighboring points).
  • Altres:

    Autor segons l'article: Latorre-Carmona, Pedro; Martinez Sotoca, Jose; Pla, Filiberto; Bioucas-Dias, Jose; Julia Ferre, Carme;
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Julià Ferré, Maria Carmen
    Paraules clau: Variable selection Regression Optimization Noise Models Land Imagery Hyperspectral datasets Feature selection Errors Classification
    Resum: This paper presents a comparative analysis of six band selection methods applied to hyperspectral datasets for biophysical variable estimation problems, where the effect of denoising on band selection performance has also been analyzed. In particular, we consider four hyperspectral datasets and three regressors of different nature (epsilon-SVR, Regression Trees, and Kernel Ridge Regression). Results show that the denoising approach improves the band selection quality of all the tested methods. We show that noise filtering is more beneficial for the selection methods that use an estimator based on the whole dataset for the prediction of the output than for methods that use strategies based on local information (neighboring points).
    Àrees temàtiques: Saúde coletiva Remote sensing Planejamento urbano e regional / demografia Odontología Matemática / probabilidade e estatística Interdisciplinar Imaging science & photographic technology Geography, physical Geografía Geociências Engineering, electrical & electronic Engenharias iv Engenharias iii Engenharias i Computers in earth sciences Ciências ambientais Ciências agrárias i Ciência da computação Biodiversidade Atmospheric science
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: carme.julia@urv.cat
    Identificador de l'autor: 0000-0003-3440-6175
    Data d'alta del registre: 2023-05-20
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://ieeexplore.ieee.org/document/6461428
    Referència a l'article segons font original: Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. 6 (2): 473-481
    Referència de l'ítem segons les normes APA: Latorre-Carmona, Pedro; Martinez Sotoca, Jose; Pla, Filiberto; Bioucas-Dias, Jose; Julia Ferre, Carme; (2013). Effect of Denoising in Band Selection for Regression Tasks in Hyperspectral Datasets. Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, 6(2), 473-481. DOI: 10.1109/JSTARS.2013.2241022
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1109/JSTARS.2013.2241022
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2013
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Atmospheric Science,Computers in Earth Sciences,Engineering, Electrical & Electronic,Geography, Physical,Imaging Science & Photographic Technology,Remote Sensing
    Variable selection
    Regression
    Optimization
    Noise
    Models
    Land
    Imagery
    Hyperspectral datasets
    Feature selection
    Errors
    Classification
    Saúde coletiva
    Remote sensing
    Planejamento urbano e regional / demografia
    Odontología
    Matemática / probabilidade e estatística
    Interdisciplinar
    Imaging science & photographic technology
    Geography, physical
    Geografía
    Geociências
    Engineering, electrical & electronic
    Engenharias iv
    Engenharias iii
    Engenharias i
    Computers in earth sciences
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biodiversidade
    Atmospheric science
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