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A framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([15/06/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [15/06/2021] . - pp 1276 - 1291 Titre : A framework for classification of volunteered geographic data based on user’s need Type de document : Article/Communication Auteurs : Nazila Mohammadi, Auteur ; Amin Sedaghat, Auteur Année de publication : 2021 Article en page(s) : pp 1276 - 1291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse en composantes principales [Termes IGN] approche participative [Termes IGN] classification par réseau neuronal [Termes IGN] données localisées des bénévoles [Termes IGN] indicateur de qualité [Termes IGN] OpenStreetMap [Termes IGN] Perceptron multicouche [Termes IGN] qualité des données [Termes IGN] zone urbaine Résumé : (auteur) VGI is an attractive source of data, but the quality assurance limits its usages. This study proposes a framework to estimate the quality of the VGI and to classify them based on the user’s need. For this purpose, a set of properties is defined to describe the data in various aspects. The principal component analysis (PCA) method is applied to reach a new set of uncorrelated indicators (UI). Volunteered data is classified based on the user’s need and takes a quality index (QI). UI and QI values are used to train the ANN. Finally, the trained ANN determines the output of the network in a way that returns QI using the UI as inputs. The proposed method was applied to estimate the quality classes of VGI in a part of an urban area. According to the results of the confusion matrix, the total accuracy of the proposed framework was 81.6%. Numéro de notice : A2021-436 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1641562 Date de publication en ligne : 16/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1641562 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97806 [article]

Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching / Amin Sedaghat in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 62 – 71 Titre : Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching Type de document : Article/Communication Auteurs : Amin Sedaghat, Auteur ; Hamid Ebadi, Auteur Année de publication : 2015 Article en page(s) : pp 62 – 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] extraction automatique [Termes IGN] image Geoeye [Termes IGN] image IRS [Termes IGN] image Landsat-ETM+ [Termes IGN] image multicapteur [Termes IGN] image Quickbird [Termes IGN] image SPOT 4 [Termes IGN] image SPOT 5 [Termes IGN] image SPOT 6 [Termes IGN] image Terra-ASTER [Termes IGN] image Worldview [Termes IGN] invariant [Termes IGN] SIFT (algorithme) Résumé : (auteur) Robust, well-distributed and accurate feature matching in multi-sensor remote sensing image is a difficult task duo to significant geometric and illumination differences. In this paper, a robust and effective image matching approach is presented for multi-sensor remote sensing images. The proposed approach consists of three main steps. In the first step, UR-SIFT (Uniform robust scale invariant feature transform) algorithm is applied for uniform and dense local feature extraction. In the second step, a novel descriptor namely Distinctive Order Based Self Similarity descriptor, DOBSS descriptor, is computed for each extracted feature. Finally, a cross matching process followed by a consistency check in the projective transformation model is performed for feature correspondence and mismatch elimination. The proposed method was successfully applied for matching various multi-sensor satellite images as: ETM+, SPOT 4, SPOT 5, ASTER, IRS, SPOT 6, QuickBird, GeoEye and Worldview sensors, and the results demonstrate its robustness and capability compared to common image matching techniques such as SIFT, PIIFD, GLOH, LIOP and LSS. Numéro de notice : A2015-852 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.06.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79222 [article]

Accurate affine invariant image matching using oriented least square / Amin Sedaghat in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 9 (September 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 9 (September 2015) . - pp 733 - 793 Titre : Accurate affine invariant image matching using oriented least square Type de document : Article/Communication Auteurs : Amin Sedaghat, Auteur ; Hamid Ebadi, Auteur Année de publication : 2015 Article en page(s) : pp 733 - 793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement automatique [Termes IGN] appariement d'images [Termes IGN] corrélation à l'aide de traits caractéristiques [Termes IGN] corrélation par moindres carrés [Termes IGN] détection de coins Harris [Termes IGN] extraction de traits caractéristiques [Termes IGN] MSER (algorithme) Résumé : (auteur) Image matching is a vital process for many photogrammetric and remote sensing applications such as image registration and aerial triangulation. In this paper, an accurate affine invariant image matching approach is presented. The proposed approach consists of three main steps. In the first step, two affine invariant feature detectors, including MSER and Harris-Affine features are applied for feature extraction. In the second step, initial corresponding features are selected using Euclidean distance between feature descriptors, followed by a consistency check process. Finally to overcome low positional accuracy of the local affine feature, an advanced version of the least square matching (LSM) namely, Oriented Least Square Matching (OLSM) is developed. Wellknown LSM method has been widely accepted as one of the most accurate methods to obtain high reliable corresponding points from a stereo image pair. However, it is sensitive to significant geometric distortion and requires very good initial approximation. In the proposed OLSM method, shape and size of the matching window are appropriately approximated using obtained affine shape information of the initial elliptical feature pairs. The proposed method was successfully applied for matching various synthetic and real close range and satellite images. Results demonstrate its accuracy and capability compared to standard LSM method Numéro de notice : A2015-986 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.9.733 En ligne : https://doi.org/10.14358/PERS.81.9.733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80267 [article]

Very high resolution image matching based on local features and k-means clustering / Amin Sedaghat in Photogrammetric record, vol 30 n° 150 (June - August 2015)  

Public [article]  inPhotogrammetric record > vol 30 n° 150 (June - August 2015) . - pp 166 - 186 Titre : Very high resolution image matching based on local features and k-means clustering Type de document : Article/Communication Auteurs : Amin Sedaghat, Auteur ; Hamid Ebadi, Auteur Année de publication : 2015 Article en page(s) : pp 166 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] distance euclidienne [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à très haute résolution [Termes IGN] partitionnement [Termes IGN] primitive géométrique Résumé : (Auteur) Image matching is a critical process in photogrammetry and remote sensing. Automatic and reliable feature matching using well-distributed points in very high resolution images is a difficult task due to significant relief displacement caused by tall buildings and ground relief. In this paper a robust and efficient image-matching approach is proposed, consisting of two main steps. In the first step, three sets of local features – Harris points, UR-SIFT and MSER – are extracted over the entire image. A SIFT (scale-invariant feature transform) descriptor is then created for each extracted feature, and an initial cross-matching verification is performed using the Euclidean distance between feature descriptors. In the second step, an approach based on k-means clustering is performed to achieve accurate matching without mismatched features, followed by a consistency check using a local affine transformation model for each cluster. The proposed method is successfully applied to matching various aerial and satellite images and the results demonstrate its robustness and capability. Numéro de notice : A2015-366 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12101 Date de publication en ligne : 29/06/2015 En ligne : https://doi.org/10.1111/phor.12101 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76908 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
106-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible