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Parameterizing support vector machines for land cover classification / X. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 27 - 37 Titre : Parameterizing support vector machines for land cover classification Type de document : Article/Communication Auteurs : X. Yang, Auteur Année de publication : 2011 Article en page(s) : pp 27 - 37 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-TM [Termes IGN] occupation du sol [Termes IGN] séparateur à vaste marge Résumé : (Auteur) The support vector machine is a group of relatively novel statistical learning algorithms that have not been extensively exploited in the remote sensing community. In previous studies they have been found to generally outperform some popular classifiers. Several recent studies found that training samples and input data dimensionalities can affect image classification accuracies by those popular classifiers and support vector machines alike. The current study extends beyond these recent research frameworks and into another important inquiry area addressing the impacts of internal parameterization on the performance of support vector machines for land-cover classification. A set of support vector machines with different combinations of kernel types, parameters, and error penalty are carefully constructed to classify a Landsat Thematic Mapper image into eight major land-cover categories using identical training data. The accuracy of each classified map is further evaluated using identical reference data. The results reveal that kernel types and error penalty can substantially affect the classification accuracy, and that a careful selection of parameter settings can help improve the performance of the support vector classification. These findings reported here can help establish a practical guidance on the use of support vector machines for land-cover classification from remote sensor data. Numéro de notice : A2011-002 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.27 En ligne : https://doi.org/10.14358/PERS.77.1.27 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30784 [article]

Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules / K. Germaine in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 75 - 85 Titre : Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules Type de document : Article/Communication Auteurs : K. Germaine, Auteur ; M.C. Hung, Auteur Année de publication : 2011 Article en page(s) : pp 75 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification ISODATA [Termes IGN] données lidar [Termes IGN] image multibande [Termes IGN] Nebraska (Etats-Unis) [Termes IGN] précision de la classification [Termes IGN] surface imperméable [Termes IGN] système à base de connaissances [Termes IGN] système expert Résumé : (Auteur) An attempt to delineate impervious surfaces in the City of Scottsbluff, Nebraska, was made using multispectral high spatial resolution imagery and lidar data. An isodata classification was performed and results aggregated into two parent classes, impervious and pervious. The ISODATA classification yielded an overall accuracy of 91.0 percent with a Kappa of 82.0 percent. A Knowledge Based Expert System (kbes) set of rules was designed incorporating the imagery classification with lidar data to derive two models, Cover Height and Cover Slope, to provide critical information not available from multispectral imagery. The rules were applied to the initial isodata classification to improve the classification accuracy to an overall accuracy of 94.0 percent with a Kappa of 87.9 percent. In this study, it was shown that lidar holds promise for improving the accuracy of impervious surface measurement, as well as the potential identification and measurement of other significant planimetric features such as buildings and trees. Numéro de notice : A2011-003 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.75 En ligne : https://doi.org/10.14358/PERS.77.1.75 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30785 [article]

Precise georeferencing of long strips of ALOS imagery / Clive Simpson Fraser in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 87 - 93 Titre : Precise georeferencing of long strips of ALOS imagery Type de document : Article/Communication Auteurs : Clive Simpson Fraser, Auteur ; M. Ravanbakhsh, Auteur Année de publication : 2011 Article en page(s) : pp 87 - 93 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] compensation par bloc [Termes IGN] géoréférencement [Termes IGN] image ALOS-PRISM [Termes IGN] modèle géométrique de prise de vue [Termes IGN] modèle par fonctions rationnelles [Termes IGN] point d'appui Résumé : (Auteur) The main obstacle to achieving high precision in georeferencing from high-resolution satellite imagery (hrsi) remains the need for provision of good quality ground control points (GCPs), whether the GCPs are used to remove biases in RFC triangulation or to support physical sensor orientation models. The provision of GCPs can be very costly and is often not feasible in remote regions, the very areas where mapping from satellite imagery shows significant potential. In order to drastically reduce the number of GCPs required for georeferencing from HRSI, a generic sensor orientation model incorporating strip adjustment capability has been adopted. Under this approach, the metadata for each separate scene is merged to produce a single, continuous set of orbit and attitude parameters, such that the entire strip of tens of images can be treated as a single image. The merging of orbit data results in a considerable reduction in both the number of unknown parameters and the number of required GCPs in the sensor orientation. RPCs are then generated from the adjusted orientation data for each image forming the strip or block. Application of the method to very long strips of ALOS PRISM imagery is reported in this paper. The results of experimental testing indicate that one-pixel level accuracy can be achieved over strip lengths of more than 50 ALOS images, or 1,500 km, with as few as four GCPs. Numéro de notice : A2011-004 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.87 En ligne : https://doi.org/10.14358/PERS.77.1.87 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30786 [article]