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Satellite image classification using granular neural networks / D. Stathakis in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n°18 - 19 - 20 (October 2006) . - pp 3991 - 4003 Titre : Satellite image classification using granular neural networks Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; A. Vasilakos, Auteur Année de publication : 2006 Article en page(s) : pp 3991 - 4003 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification floue [Termes IGN] classification par réseau neuronal [Termes IGN] granularité d'image [Termes IGN] image IRS-LISS Résumé : (Auteur) The increased synergy between neural networks (NN) and fuzzy sets has led to the introduction of granular neural networks (GNNs) that operate on granules of information, rather than information itself. The fact that processing is done on a conceptual rather than on a numerical level, combined with the representation of granules using linguistic terms, results in increased interpretability. This is the actual benefit, and not increased accuracy, gained by GNNs. The constraints used to implement the GNN are such that accuracy degradation should not be surprising. Having said that, it is well known that simple structured NNs tend to be less prone to over-fitting the training data set, maintaining the ability to generalize and more accurately classify previously unseen data. Standard NNs are frequently found to be accurate but difficult to explain, hence they are often associated with the black box syndrome. Because in GNNs the operation is carried out at a conceptual level, the components have unambiguous meaning, revealing how classification decisions are formed. In this paper, the interpretability of GNNs is exploited using a satellite image classification problem. We examine how land use classification using both spectral and non-spectral information is expressed in GNN terms. One further contribution of this paper is the use of specific symbolization of the network components to easily establish causality relationships. Copyright Taylor & Francis Numéro de notice : A2006-460 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600567779 En ligne : https://doi.org/10.1080/01431160600567779 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28184 [article]

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Comparison of computational intelligence based classification techniques for remotely sensed optical image classification / D. Stathakis in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2305 - 2318 Titre : Comparison of computational intelligence based classification techniques for remotely sensed optical image classification Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; A. Vasilakos, Auteur Année de publication : 2006 Article en page(s) : pp 2305 - 2318 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] analyse comparative [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] classification par algorithme génétique [Termes IGN] classification par réseau neuronal [Termes IGN] image optique [Termes IGN] occupation du sol Résumé : (Auteur) Several computational intelligence components, namely neural networks (NNs), fuzzy sets, and genetic algorithms (GAs), have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of NNs, optimal NN structure and parameter determination via GAs, and transparency using fuzzy sets is expected. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. A comparison of the configurations is achieved by testing the different methods with exactly the same case-study data. A thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness, and consistency. The architecture, produced rule set, and training parameters for the specific classification task are presented. Some comments and directions for future work are given. Copyright IEEE Numéro de notice : A2006-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872903 En ligne : https://doi.org/10.1109/TGRS.2006.872903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28121 [article]

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