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Localized soft classification for super-resolution mapping of the shoreline / Aidy M. Muslim in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n° 11 (June 2006) . - pp 2271 - 2285 Titre : Localized soft classification for super-resolution mapping of the shoreline Type de document : Article/Communication Auteurs : Aidy M. Muslim, Auteur ; Giles M. Foody, Auteur ; P.M. Atkinson, Auteur Année de publication : 2006 Article en page(s) : pp 2271 - 2285 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse infrapixellaire [Termes IGN] carte topographique [Termes IGN] histogramme [Termes IGN] image Ikonos [Termes IGN] Malaisie [Termes IGN] trait de côte Résumé : (Auteur) The Malaysian shoreline is dynamic and constantly changing in location. Although the shoreline may be mapped accurately from fine spatial resolution imagery, this is an impractical approach for use over large areas. An alternative approach using coarse spatial resolution satellite sensor imagery is to fit a shoreline boundary at sub-pixel scale. This paper evaluates the use of soft classification and super-resolution mapping techniques to accurately map the shoreline. A localized soft classification approach was used to provide an accurate prediction of the thematic composition of each image pixel. This involves the use of training statistics derived locally rather than globally in the classification. Using the derived class proportion information the shoreline boundary was determined within the pixels using super-resolution techniques. Results show that by using a localized approach in the prediction of the pixel's thematic class composition, the accuracy of shoreline prediction was increased. Notably, the use of the localized approach resulted in the shoreline with an rms error of Numéro de notice : A2006-300 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500396741 En ligne : https://doi.org/10.1080/01431160500396741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28027 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06061	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Deriving ground surface digital elevation models from Lidar data with geostatistics / C.D. Lloyd in International journal of geographical information science IJGIS, vol 20 n° 5 (may 2006)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 20 n° 5 (may 2006) . - pp 535 - 563 Titre : Deriving ground surface digital elevation models from Lidar data with geostatistics Type de document : Article/Communication Auteurs : C.D. Lloyd, Auteur ; P.M. Atkinson, Auteur Année de publication : 2006 Article en page(s) : pp 535 - 563 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] géostatistique [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] krigeage [Termes IGN] lissage de données [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] surface du sol Résumé : (Auteur) This paper focuses on two common problems encountered when using Light Detection And Ranging (LiDAR) data to derive digital elevation models (DEMs). Firstly, LiDAR measurements are obtained in an irregular configuration and on a point, rather than a pixel, basis. There is usually a need to interpolate from these point data to a regular grid so it is necessary to identify the approaches that make best use of the sample data to derive the most accurate DEM possible. Secondly, raw LiDAR data contain information on above-surface features such as vegetation and buildings. It is often the desire to (digitally) remove these features and predict the surface elevations beneath them, thereby obtaining a DEM that does not contain any above-surface features. This paper explores the use of geostatistical approaches for prediction in this situation. The approaches used are inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT). It is concluded that, for the case studies presented, OK offers greater accuracy of prediction than IDW while KT demonstrates benefits over OK. The absolute differences are not large, but to make the most of the high quality LiDAR data KT seems the most appropriate technique in this case. Numéro de notice : A2006-175 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810600607337 En ligne : https://doi.org/10.1080/13658810600607337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27902 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-06051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-06052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Relating SAR image texture to the biomass of regenerating tropical forests / T.M. Kuplich in International Journal of Remote Sensing IJRS, vol 26 n° 21 (November 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 21 (November 2005) . - pp 4829 - 4854 Titre : Relating SAR image texture to the biomass of regenerating tropical forests Type de document : Article/Communication Auteurs : T.M. Kuplich, Auteur ; P.J. Curran, Auteur ; P.M. Atkinson, Auteur Année de publication : 2005 Article en page(s) : pp 4829 - 4854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande L [Termes IGN] canopée [Termes IGN] forêt tropicale [Termes IGN] image JERS [Termes IGN] image radar [Termes IGN] Manaus [Termes IGN] masse végétale [Termes IGN] niveau de gris (image) [Termes IGN] teneur en carbone [Termes IGN] texture d'image [Termes IGN] variogramme Résumé : (Auteur) An accurate global carbon budget requires information on terrestrial carbon sink strength. Regenerating tropical forests are known to be important terrestrial carbon sinks but information on their location, extent and biomass (from which carbon content can be estimated) is incomplete. The use of remotely sensed data in optical wavelengths has been of limited use due to both the weak relationship between optical radiation and forest biomass and near-constant cloud cover in the tropics. L-band Synthetic Aperture Radar (SAR) backscatter, however, is related positively to biomass (but only up to an asymptote of around 40-90T ha-1) and can be obtained independently of cloud cover. Both canopy structure and biomass change over time as pioneer species are replaced by early and late regenerating species. These structural changes are related to an increase in (i) tree height, (ii) tree species richness and (iii) canopy thickness and influence the roughness of the canopy surface and consequently SAR image texture. Therefore, we investigated the degree to which textural information could be used to increase the correlation between image tone (backscatter) and biomass. Field data were used to estimate the biomass of 37 regenerating forests plots in Brazilian Amazonia. Texture measures derived from local statistics, the grey level co-occurrence matrix (GLCM) and the variogram were evaluated using simulated images on the basis of their ability to identify significant differences in image texture independently of image contrast. The selected texture measures were applied to L-band JERS-1 (Japanese Earth Resources Satellite) SAR images and the correlation between backscatter and biomass was determined for regenerating tropical forests. A strong correlation was found for the texture measures and biomass. The ra2 (adjusted coefficient of determination), measuring the correlation between backscatter and biomass, increased from 0.74 to 0.82 with the addition of GLCM-derived contrast. The addition of image texture (GLCM-derived contrast) to image tone (backscatter) potentially increases the accuracy with which JERS-1 SAR data can be used to estimate biomass in tropical forests. Numéro de notice : A2005-469 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500239107 En ligne : https://doi.org/10.1080/01431160500239107 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27605 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05211	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 17 n° 7 (october 2003) . - pp 647 - 672 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 infrapixellaire Résumé : (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 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-03071	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-03072	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Advances in remote sensing and GIS analysis, [selected papers from a meeting held at the University of Southampton, July 25, 1996] / P.M. Atkinson (1999)

Public Titre : Advances in remote sensing and GIS analysis, [selected papers from a meeting held at the University of Southampton, July 25, 1996] Type de document : Actes de congrès Auteurs : P.M. Atkinson, Éditeur scientifique ; Nicholas J. Tate, Éditeur scientifique Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 1999 Importance : 273 p. Format : 18 x 25 cm ISBN/ISSN/EAN : 978-0-471-98577-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatiale [Termes IGN] atmosphère terrestre [Termes IGN] bruit (théorie du signal) [Termes IGN] capteur spatial [Termes IGN] classification floue [Termes IGN] classification par réseau neuronal [Termes IGN] géologie [Termes IGN] géophysique [Termes IGN] image à très haute résolution [Termes IGN] image numérique [Termes IGN] modélisation [Termes IGN] occupation du sol [Termes IGN] réalité virtuelle [Termes IGN] système d'information géographique [Termes IGN] urbanisme [Termes IGN] utilisation du sol Index. décimale : 35.40 Applications de télédétection - généralités Résumé : (Editeur) Remote sensing and GIS are technologies for the new millennium. This book brings together some of the most recent developments in these complimentary fields, with a particular emphasis on mathematical techniques and their application. The range of techniques covered includes fuzzy classification, artificial neural networks, geostatistical techniques (such as kriging, cokriging , stochastic simulation and regularization), texture classification, fractals, per-parcel classification, raster and vector data integration and process modelling. The range of applications includes land cover and land use mapping, cloud tracking, snow cover mapping and air temperature monitoring, topographic mapping, geological classification and soil erosion modelling. . Note de contenu : PART ONE: ON GEOCOMPUTATION 1 Foundations / Paul A Longley 2 Geocomputation in Context / Helen Couclelis PART TWO: ON DATA AND DOCUMENTATION 3 Remote Sensing : From Data to Understanding / Paul J Curran, Edward J Milton, Peter M Atkinson and Giles M Foody 4 Different Data Sources and Diverse Data Structures : Metadata and Other Solutions / Michael F. Goodchild PART THREE: DIAGNOSTICS AND PATTERN DETECTION 5 Exploratory Spatial Data Analysis in a Geocomputational Environment / Luc Anselin 6 Building Automated Geographical Analysis and Explanation Machines / Stan Openshaw PART FOUR: REAL AND VIRTUAL ENVIRONMENTS 7 Visualising Different Geofutures / Keith C Clarke 8 Modelling Virtual Environments / Michael Batty, Martin Dodge, Simon Doyle and Andy Smith PART FIVE: SPACETIME DYNAMICS 9 Dynamic Modelling and Geocomputation / Peter A Burrough 10 On the Status and Opportunities for Physical Process Modelling in Geomorphology / S.M. Brooks and M. G. Anderson 11 On Complex Geographical Space : Computing Frameworks for Spatial Diffusion Processes / Andrew D. Cliff and Peter Haggett Numéro de notice : 11726 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Actes Accessibilité hors numérique : Accessible via le SUDOC (sur demande au cdos) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=40049