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Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)  

Public [article]  inGeocarto international > vol 33 n° 10 (October 2018) . - pp 1017 - 1035 Titre : Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier Type de document : Article/Communication Auteurs : Huanxue Zhang, Auteur ; Qiangzi Li, Auteur ; Jiangui Liu, Auteur ; Taifeng Dong, Auteur ; Heather McNairn, Auteur Année de publication : 2018 Article en page(s) : pp 1017 - 1035 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] bande spectrale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] corrélation par régions de niveaux de gris [Termes IGN] extraction de traits caractéristiques [Termes IGN] image SPOT 5 [Termes IGN] indice de végétation [Termes IGN] limite de terrain [Termes IGN] Ontario (Canada) [Termes IGN] réflectance spectrale [Termes IGN] segmentation d'image [Termes IGN] surface cultivée [Termes IGN] surveillance agricole [Termes IGN] texture d'image [Termes IGN] variogramme Résumé : (auteur) In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification. Numéro de notice : A2019-049 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1333533 Date de publication en ligne : 23/06/2017 En ligne : https://doi.org/10.1080/10106049.2017.1333533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92063 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)  

Public [article]  inGeocarto international > vol 33 n° 10 (October 2018) . - pp 1036 - 1047 Titre : Automated extraction of 3D vector topographic feature line from terrain point cloud Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Rencan Peng, Auteur ; Jian Dong, Auteur ; Tao Wang, Auteur Année de publication : 2018 Article en page(s) : pp 1036 - 1047 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre aléatoire minimum [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] ligne caractéristique [Termes IGN] lissage de données [Termes IGN] modèle numérique de terrain [Termes IGN] objet géographique linéaire [Termes IGN] repère de Laplace [Termes IGN] segmentation en régions [Termes IGN] semis de points Résumé : (auteur) This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm. Numéro de notice : A2019-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1325521 Date de publication en ligne : 18/05/2017 En ligne : https://doi.org/10.1080/10106049.2017.1325521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92064 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)  

Public [article]  inGeocarto international > vol 33 n° 10 (October 2018) . - pp 1139 - 1154 Titre : Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 Type de document : Article/Communication Auteurs : Umut Gunes Sefercik, Auteur ; Serkan Karakis, Auteur ; Can Atalay, Auteur ; Ibrahim Yigit, Auteur ; Umit Gokmen, Auteur Année de publication : 2018 Article en page(s) : pp 1139 - 1154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] détection d'objet [Termes IGN] détection du bâti [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction du réseau routier [Termes IGN] filtre de Wallis [Termes IGN] image numérique [Termes IGN] image Worldview [Termes IGN] modèle numérique de surface [Termes IGN] Turquie Résumé : (auteur) The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads. Numéro de notice : A2019-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1353646 Date de publication en ligne : 27/07/2017 En ligne : https://doi.org/10.1080/10106049.2017.1353646 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92068 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible