Centre de documentation
scientifique

On the relationship between training sample size data dimensionality: Monte Carlo analysis of broadland multi-temporal classification / T.G. Van Niel in Remote sensing of environment, vol 98 n° 4 (30/10/2005)  

Public [article]  inRemote sensing of environment > vol 98 n° 4 (30/10/2005) . - pp 468 - 480 Titre : On the relationship between training sample size data dimensionality: Monte Carlo analysis of broadland multi-temporal classification Type de document : Article/Communication Auteurs : T.G. Van Niel, Auteur ; T.R. Mcvicar, Auteur Année de publication : 2005 Article en page(s) : pp 468 - 480 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] classification par maximum de vraisemblance [Termes IGN] échantillonnage d'image [Termes IGN] figure géométrique [Termes IGN] image Landsat-ETM+ [Termes IGN] image multitemporelle [Termes IGN] méthode de Monte-Carlo Résumé : (Auteur) The number of training samples per class (n) required for accurate Maximum Likelihood (ML) classification is known to be affected by the number of bands (p) in the input image. However, the general rule which defines that n should be 10p to 30p is often enforced universally in remote sensing without questioning its relevance to the complexity of the specific discrimination problem. Furthermore, identifying this many training samples is often problematic when many classes and/or many bands are used. It is important, then, to test how this generally accepted rule matches common remote sensing discrimination problems because it could be unnecessarily restrictive for many applications. This study was primarily conducted in order to test whether the general rule defining the relationship between n and p was well-suited for ML classification of a relatively simple remote sensing-based discrimination problem. To summarise the mean response of n-to-p for our study site, a Monte Carlo procedure was used to randomly stack various numbers of bands into thousands of separate image combinations that were then classified using an ML algorithm. The bands were randomly selected from a 119-band Enhanced Thematic Mapper-plus (ETM+) dataset comprised of 17 images acquired during the 2001-2002 southern hemisphere summer agricultural growing season over an irrigation area in south-eastern Australia. Results showed that the number of training samples needed for accurate ML classification was much lower than the current widely accepted rule. Due to the asymptotic nature of the relationship, we found that 95% of the accuracy attained using n = 30p samples could be achieved by using approximately 2p to 4p samples, or Numéro de notice : A2005-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2005.08.011 En ligne : https://doi.org/10.1016/j.rse.2005.08.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27570 [article]