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Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network / A.J. Tatem in International journal of geographical information science IJGIS, vol 17 n° 7 (october 2003)
[article]
Titre : Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network Type de document : Article/Communication Auteurs : A.J. Tatem, Auteur ; H.G. Lewis, Auteur ; P.M. Atkinson, Auteur ; M.S. Nixon, Auteur Année de publication : 2003 Article en page(s) : pp 647 - 672 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte agricole
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal
[Termes IGN] erreur moyenne quadratique
[Termes IGN] Grèce
[Termes IGN] image Landsat-TM
[Termes IGN] image satellite
[Termes IGN] incertitude géométrique
[Termes IGN] limite de résolution géométrique
[Termes IGN] occupation du sol
[Termes IGN] précision infrapixellaireRésumé : (Auteur) Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a 'best guess' prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the subpixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of smallscale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of pratical remotly sensed imagery at the sub pixel scale. Numéro de notice : A2003-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/1365881031000135519 En ligne : https://doi.org/10.1080/1365881031000135519 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22553
in International journal of geographical information science IJGIS > vol 17 n° 7 (october 2003) . - pp 647 - 672[article]Réservation
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