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Auteur M.J. Aitkenhead |
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Improving land-cover classification using recognition threshold neural networks / M.J. Aitkenhead in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 4 (April 2007)
[article]
Titre : Improving land-cover classification using recognition threshold neural networks Type de document : Article/Communication Auteurs : M.J. Aitkenhead, Auteur ; R. Dyer, Auteur Année de publication : 2007 Article en page(s) : pp 413 - 421 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] image Landsat
[Termes IGN] Philippines
[Termes IGN] seuillage d'image
[Termes IGN] surface cultivéeRésumé : (Auteur) The use of neural networks to classify land-cover from remote sensing imagery relies on the ability to determine a winner from the candidate land-cover types based on the imagery information available. In the case of a “winner- takes-all” scenario, this does not allow us a measure of how much the prediction of each pixel’s land-cover can be trusted. We present a three-stage method where only winning candidates which are given a clear lead over the other land-cover types are accepted, with a neighborhood relationship and the application of mixed pixels being used to provide full classification. This method allows us to place more faith in the resulting map than simply taking the winner, and results in a higher accuracy of classification. The method is applied to Landsat imagery of an area of the Philippines where natural, urban, and cultivated land-cover types exist. Copyright ASPRS Numéro de notice : A2007-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.4.413 En ligne : https://doi.org/10.14358/PERS.73.4.413 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28506
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 4 (April 2007) . - pp 413 - 421[article]Mapping land cover from detailed aerial photography data using textural and neural network analysis / R. Cots-Folch in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)
[article]
Titre : Mapping land cover from detailed aerial photography data using textural and neural network analysis Type de document : Article/Communication Auteurs : R. Cots-Folch, Auteur ; M.J. Aitkenhead, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 1625 - 1642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse texturale
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal
[Termes IGN] image aérienne
[Termes IGN] paysage agricole
[Termes IGN] photographie panchromatique
[Termes IGN] utilisation du solRésumé : (Auteur) Automated mapping of land cover using black and white aerial photographs, as an alternative method to traditional photo-interpretation, requires using methods other than spectral analysis classification. To this end, textural measurements have been shown to be useful indicators of land cover. In this work, a neural network model is proposed and tested to map historical land use/land cover (LUC) from very detailed panchromatic aerial photographs (5m resolution) using textural measurements. The method is used to identify different land use and management types (e.g. traditional versus mechanized vineyard systems). These have been tested with known ground reference data. The results show the potential of the methodology to obtain automatic, historic, and very detailed cartography information from a complex landscape such as the mountainous and Mediterranean region to which it is applied here, and the advantages that this method has over traditional methods. Copyright Taylor & Francis Numéro de notice : A2007-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600887722 En ligne : https://doi.org/10.1080/01431160600887722 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28540
in International Journal of Remote Sensing IJRS > vol 28 n°7-8 (April 2007) . - pp 1625 - 1642[article]Exemplaires(1)
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