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Artificial immune-based supervised classifier for land-cover classification / M. Pal in International Journal of Remote Sensing IJRS, vol 29 n° 7 (April 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n° 7 (April 2008) . - pp 2273 - 2291 Titre : Artificial immune-based supervised classifier for land-cover classification Type de document : Article/Communication Auteurs : M. Pal, Auteur Année de publication : 2008 Article en page(s) : pp 2273 - 2291 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] classification par arbre de décision [Termes IGN] classification par maximum de vraisemblance [Termes IGN] identification automatique [Termes IGN] image Landsat-ETM+ [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] système immunitaire artificiel Résumé : (Auteur) This paper explores the potential of an artificial immune-based supervised classification algorithm for land-cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non-self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune-based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune-based classifier works well in comparison with the maximum likelihood and the decision-tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision-tree classification algorithms. Results suggest an improved performance with the immune-based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune-based supervised classifier requires several user-defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant. Copyright Taylor & Francis Numéro de notice : A2008-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701408402 En ligne : https://doi.org/10.1080/01431160701408402 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29095 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Some issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n°12-13-14 (July 2006) . - pp 2895 - 2916 Titre : Some issues in the classification of DAIS hyperspectral data Type de document : Article/Communication Auteurs : M. Pal, Auteur ; Paul M. Mather, Auteur Année de publication : 2006 Article en page(s) : pp 2895 - 2916 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classificateur paramétrique [Termes IGN] classification dirigée [Termes IGN] classification par arbre de décision [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Espagne [Termes IGN] image DAIS [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification [Termes IGN] qualité du processus [Termes IGN] transformation orthogonale Résumé : (Auteur) Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, support vector machines and decision tree). There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases. The support vector machine classifier performed well with all test data sets. The use of the orthogonal MNF transform resulted in a decline in classification accuracy. However, the decision-tree approach to feature selection worked well. Small increases in classifier accuracy may be obtained using more sophisticated techniques, but it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image. Copyright Taylor & Francis Numéro de notice : A2006-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500185227 En ligne : https://doi.org/10.1080/01431160500185227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28033 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06071	RAB	Revue	Centre de documentation	En réserve L003	Disponible