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Combining spectral and spatial information into hidden Markov models for unsupervised image classification / B. Tso in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2113 - 2133 Titre : Combining spectral and spatial information into hidden Markov models for unsupervised image classification Type de document : Article/Communication Auteurs : B. Tso, Auteur ; C. Olsen, Auteur Année de publication : 2005 Article en page(s) : pp 2113 - 2133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification barycentrique [Termes IGN] classification contextuelle [Termes IGN] classification non dirigée [Termes IGN] données localisées 2D [Termes IGN] image multi sources [Termes IGN] modèle de Markov [Termes IGN] optimisation (mathématiques) [Termes IGN] précision de la classification [Termes IGN] qualité des données Résumé : (Auteur) Unsupervised classification methodology applied to remote sensing image processing can provide benefits in automatically converting the raw image data into useful information so long as higher classification accuracy is achieved. The traditional k-means clustering scheme using spectral data alone does not perform well in general as far as accuracy is concerned. This is partly due to the failure to take the spatial inter-pixels dependencies (i.e. the context) into account, resulting in a 'busy' visual appearance to the output imagery. To address this, the hidden Markov models (HMM) are introduced in this study as a fundamental framework to incorporate both the spectral and contextual information in analysis. This helps generate more patch-like output imagery and produces higher classification accuracy in an unsupervised scheme. The newly developed unsupervised classification approach is based on observation-sequence and observation-density adjustments, which have been proposed for incorporating 2D spatial information into the linear HMM. For the observation-sequence adjustment methods, there are a total of five neighbourhood systems being proposed. Two neighbourhood systems were incorporated into the observation-density methods for study. The classification accuracy is then evaluated by means of confusion matrices made by randomly chosen test samples. The classification obtained by k-means clustering and the HMM with commonly seen strip-like and Hilbert-Peano sequence fitting methods were also measured. Experimental results showed that the proposed approaches for combining both the spectral and spatial information into HMM unsupervised classification mechanism present improvements in both classification accuracy and visual qualities. Numéro de notice : A2005-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331337844 En ligne : https://doi.org/10.1080/01431160512331337844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27395 [article]

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