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Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)  

Public [article]  inRemote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 476 - 487 Titre : Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium Type de document : Article/Communication Auteurs : F.M.B. Van Coillie, Auteur ; L.P.C. Verbeke, Auteur ; R.R. DE Wulf, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 476 - 487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal [Termes IGN] détection d'objet [Termes IGN] Flandre (Belgique) [Termes IGN] forêt tempérée [Termes IGN] image Ikonos [Termes IGN] segmentation d'image Résumé : (Auteur) Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher (p Numéro de notice : A2007-412 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.03.020 En ligne : https://doi.org/10.1016/j.rse.2007.03.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28775 [article]

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients / K.C. Mertens in Remote sensing of environment, vol 91 n° 2 (30/05/2004)  

Public [article]  inRemote sensing of environment > vol 91 n° 2 (30/05/2004) . - pp 225 - 236 Titre : Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients Type de document : Article/Communication Auteurs : K.C. Mertens, Auteur ; L.P.C. Verbeke, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 225 - 236 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] accentuation de contours [Termes IGN] classification par réseau neuronal [Termes IGN] coefficient de corrélation [Termes IGN] image à haute résolution [Termes IGN] image de synthèse [Termes IGN] ondelette [Termes IGN] précision de la classification [Termes IGN] précision infrapixellaire Résumé : (Auteur) Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices. Numéro de notice : A2004-244 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.03.003 En ligne : https://doi.org/10.1016/j.rse.2004.03.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26771 [article]