Centre de documentation
scientifique

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

A supervised artificial immune classifier for remote-sensing imagery / Y. Zhong in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3957 - 3966 Titre : A supervised artificial immune classifier for remote-sensing imagery Type de document : Article/Communication Auteurs : Y. Zhong, Auteur ; L. Zhang, Auteur ; J. Gong, Auteur ; P. Li, Auteur Année de publication : 2007 Article en page(s) : pp 3957 - 3966 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classificateur [Termes IGN] classification dirigée [Termes IGN] image [Termes IGN] réseau neuronal artificiel [Termes IGN] système immunitaire artificiel Résumé : (Auteur) The artificial immune network (AIN), which is a new computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to remote-sensing image classification has been rather limited. This paper presents a novel supervised classification algorithm based on a multiple-valued immune network, which is a novel AIN model, to perform remote-sensing image classification. The proposed method trains the immune network using the samples of regions of interest and obtains an immune network with memory to classify the remote-sensing imagery. Two experiments with different types of images are performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: Parallelepiped, Minimum Distance, Maximum Likelihood, and Back-Propagation Neural Network. The results evince that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and, hence, provides an effective option for processing remote-sensing imagery. Copyright IEEE Numéro de notice : A2007-585 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.907739 En ligne : https://doi.org/10.1109/TGRS.2007.907739 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28948 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07121A	RAB	Revue	Centre de documentation	En réserve L003	Disponible