Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA new supervised classifier exploiting spectral-spatial information in the Bayesian framework
Anno di pubblicazione2019
Formato
  • Elettronico
  • Cartaceo
Autore/iBarca, Emanuele; Castrignano, Annamaria; Ruggieri, Sergio; Rinaldi, Michele
Affiliazioni autoriItalian Res Council IRSA CNR; Council Agr Res & Econ; Council Agr Res & Econ
Autori CNR e affiliazioni
  • EMANUELE BARCA
Lingua/e
  • inglese
AbstractConventional machine learning methods are often unable to achieve high degrees of accuracy when only spectral data are involved in the classification process. The main reason of that inaccuracy can be brought back to the omission of the spatial information in the classification. The present paper suggests a way to combine effectively the spectral and the spatial information and improve the classification's accuracy. In practice, a Bayesian two-stage methodology is proposed embodying two enhancements: i) a geostatistical non-parametric classification approach, the universal indicator kriging and the smooth multivariate kernel method. The former provides an informative prior, while the latter overcomes the assumption (often not true) of independence of the spectral data. The case study reports an application to land-cover classification in a study area located in the Apulia region (Southern Italy). The methodology performance in terms of overall accuracy was compared with five state-of-the-art methods, i.e. naive Bayes, Random Forest, artificial neural networks, support vector machines and decision trees. It is shown that the proposed methodology outperforms all the compared methods and that even a severe reduction of the training set does not affect seriously the average accuracy of the presented method.
Lingua abstractinglese
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Pagine da-
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Pagine totali14
RivistaInternational journal of applied earth observation and geoinformation
Attiva dal 1999
Editore: International Institute for Aerial Survey and Earth Sciences - Enschede
Lingua: inglese
ISSN: 1569-8432
Titolo chiave: International journal of applied earth observation and geoinformation
Titolo proprio: International journal of applied earth observation and geoinformation.
Titolo abbreviato: Int. j. appl. earth obs. geoinf.
Titoli alternativi:
  • JAG
  • ITC journal
Numero volume della rivista86
Fascicolo della rivista-
DOI10.1016/j.jag.2019.101990
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000509787800006)
  • Scopus (Codice:2-s2.0-85086763263)
Parole chiaveLand-cover classification, Bayes' method, multivariate smooth kernel, universal indicator kriging
Link (URL, URI)https://www.sciencedirect.com/science/article/pii/S0303243418310638
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Strutture CNR
  • IRSA — Istituto di ricerca sulle acque
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A new supervised classifier exploiting spectral-spatial information in the Bayesian framework (documento privato )
Tipo documento: application/pdf