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Super-resolution land cover mapping using a Markov random field based approach / T. Kasetkasem in Remote sensing of environment, vol 96 n° 3 (30/06/2005)  

Public [article]  inRemote sensing of environment > vol 96 n° 3 (30/06/2005) . - pp 302 - 314 Titre : Super-resolution land cover mapping using a Markov random field based approach Type de document : Article/Communication Auteurs : T. Kasetkasem, Auteur ; M.J. Arora, Auteur ; P.K. Varshney, Auteur Année de publication : 2005 Article en page(s) : pp 302 - 314 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] image Ikonos [Termes IGN] image Landsat-ETM+ [Termes IGN] précision infrapixellaire [Termes IGN] programmation linéaire Résumé : (Auteur) Occurrence of mixed pixels in remote sensing images is a major problem particularly at coarse spatial resolutions. Therefore, sub-pixel classification is often preferred, where a pixel is resolved into various class components (also called class proportions or fractions). While, under most circumstances, land cover information in this form is more effective than crisp classification, sub-pixel classification fails to account for the spatial distribution of class proportions within the pixel. An alternative approach is to consider the spatial distribution of class proportions within and between pixels to perform super-resolution mapping (i.e. mapping land cover at a spatial resolution finer than the size of the pixel of the image). Markov random field (MRF) models are well suited to represent the spatial dependence within and between pixels. In this paper, an MRF model based approach is introduced to generate super-resolution land cover maps from remote sensing data. In the proposed MRF model based approach, the intensity values of pixels in a particular spatial structure (i.e., neighborhood) are allowed to have higher probability (i.e., weight) than others. Remote sensing images at two markedly different spatial resolutions, IKONOS MSS image at 4m spatial resolution and Landsat ETM+ image at 30m spatial resolution, are used to illustrate the effectiveness of the proposed MRF model based approach for super-resolution land cover mapping. The results show a significant increase in the accuracy of land cover maps at fine spatial resolution over that obtained from a recently proposed linear optimization approach suggested by Verhoeye and Wulf (2002) (Verhoeye, J., Wulf, R. D. (2002). Land Cover Mapping at Sub-pixel Scales using Linear Optimization Techniques, Remote Sensing of Environment, 79, 96-104). Numéro de notice : A2005-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2005.02.006 En ligne : https://doi.org/10.1016/j.rse.2005.02.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27402 [article]

Unsupervised classification of hyperspectral data: an ICA mixture model based approach / Chintan A. Shah in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 2 (January 2004) Titre : Unsupervised classification of hyperspectral data: an ICA mixture model based approach Type de document : Article/Communication Auteurs : Chintan A. Shah, Auteur ; M.K. Arora, Auteur ; P.K. Varshney, Auteur Année de publication : 2004 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification non dirigée [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) Conventional unsupervised classification algorithms that model the data in each class with a multivariate Gaussian distribution are often inappropriate, as this assumption is frequently not satisfied by the remote sensing data. In this Letter, a new algorithm based on independent component analysis (ICA) is presented. The ICA mixture model (ICAMM) algorithm that models class distributions as non-Gaussian densities has been employed for unsupervised classification of a test image from the AVIRIS sensor. A number of feature-extraction techniques have also been examined that serve as a preprocessing step to reduce the dimensionality of the hyperspectral data. The proposed ICAMM algorithm results in significant increase in the classification accuracy over that obtained from the conventional K-means algorithm for land cover classification. Numéro de notice : A2004-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001618040 En ligne : https://doi.org/10.1080/01431160310001618040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26588 [article]

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