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Integration of classification methods for improvement of land-cover map accuracy / XiaoHang Liu in ISPRS Journal of photogrammetry and remote sensing, vol 56 n° 4 (July - August 2002)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 56 n° 4 (July - August 2002) . - pp 257 - 268 Titre : Integration of classification methods for improvement of land-cover map accuracy Type de document : Article/Communication Auteurs : XiaoHang Liu, Auteur ; Andrew K. Skidmore, Auteur ; H.V. Oosten, Auteur Année de publication : 2002 Article en page(s) : pp 257 - 268 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] carte d'occupation du sol [Termes IGN] classification à base de connaissances [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] occupation du sol Résumé : (Auteur) Classifiers, which are used to recognize patterns in remotely sensing images, have complementary capabilities. This study tested whether integrating the results from individual classifiers improves classification accuracy. Two integrated approaches were undertaken. One approach used a consensus builder (CS13) to adjust classification output in the case of disagreement in classification between maximum likelihood classifier (MLC), expert system classifier (ESC) and neural network classifier (NNC). If the output classes for each individual pixel differed, the producer accuracies for each class were compared and the class with the highest producer accuracy was assigned to the pixel. The consensus builder approach resulted in a classification with a slightly lower accuracy (72%) when compared with the neural network classifier (74%), but it did significantly better than the maximum likelihood (62%) and expert system (59%) classifiers. The second approach integrated a rulebased expert system classifier and a neural network classifier. The output of the expert system classifier was used as one additional new input layer of the neural network classifier. A postprocessing using the producer accuracies and some additional expert rules was applied to improve the output of the integrated classifier. This is a relatively new approach in the field of image processing. This second approach produced the highest overall accuracy (80%). Thus, incorporating correct, complete and relevant expert knowledge in a neural network classifier leads to higher classification accuracy. Copyright ISPRS Numéro de notice : A2002-168 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/S0924-2716(02)00061-8 En ligne : https://doi.org/10.1016/S0924-2716(02)00061-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22083 [article]

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