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Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks Type de document : Article/Communication Auteurs : Rasha Alshehhi, Auteur ; Prashanth Reddy Marpu, Auteur ; Wei Lee Woon, Auteur ; Mauro Dalla Mura, Auteur Année de publication : 2017 Article en page(s) : pp 139 - 149 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] architecture de réseau
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection du bâti
[Termes IGN] extraction automatique
[Termes IGN] extraction du réseau routier
[Termes IGN] filtrage numérique d'image
[Termes IGN] image à haute résolution
[Termes IGN] réseau neuronal convolutif
[Termes IGN] tachèle
[Termes IGN] test de performance
[Termes IGN] zone urbaineRésumé : (Auteur) Extraction of man-made objects (e.g., roads and buildings) from remotely sensed imagery plays an important role in many urban applications (e.g., urban land use and land cover assessment, updating geographical databases, change detection, etc). This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class and lower inter-class variations. In this work, we propose a single patch-based Convolutional Neural Network (CNN) architecture for extraction of roads and buildings from high-resolution remote sensing data. Low-level features of roads and buildings (e.g., asymmetry and compactness) of adjacent regions are integrated with Convolutional Neural Network (CNN) features during the post-processing stage to improve the performance. Experiments are conducted on two challenging datasets of high-resolution images to demonstrate the performance of the proposed network architecture and the results are compared with other patch-based network architectures. The results demonstrate the validity and superior performance of the proposed network architecture for extracting roads and buildings in urban areas. Numéro de notice : A2017-512 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86458
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 139 - 149[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)
[article]
Titre : Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis Type de document : Article/Communication Auteurs : Arati Paul, Auteur ; V.M. Chowdary, Auteur ; Y.K. Srivastava, Auteur ; Debsunder Dutta, Auteur ; J.R. Sharma, Auteur Année de publication : 2017 Article en page(s) : pp 640 - 654 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Calcutta
[Termes IGN] densité spectrale de puissance
[Termes IGN] détection de changement
[Termes IGN] détection de contours
[Termes IGN] Google Earth
[Termes IGN] image Cartosat-1
[Termes IGN] image IRS-LISS
[Termes IGN] occupation du sol
[Termes IGN] seuillage d'imageRésumé : (Auteur) Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively. Numéro de notice : A2017-275 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1167966 Date de publication en ligne : 01/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1167966 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85304
in Geocarto international > vol 32 n° 6 (June 2017) . - pp 640 - 654[article]Building occlusion detection from ghost images / Guoqing Zhou in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)
[article]
Titre : Building occlusion detection from ghost images Type de document : Article/Communication Auteurs : Guoqing Zhou, Auteur ; Yuefeng Wang, Auteur ; Tao Yue, Auteur Année de publication : 2017 Article en page(s) : pp 1074 - 1084 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction d'image
[Termes IGN] détection de contours
[Termes IGN] détection de partie cachée
[Termes IGN] orthoimage
[Termes IGN] orthoimage intégrale
[Termes IGN] zone tamponRésumé : (Auteur) This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation. Numéro de notice : A2017-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2619184 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2619184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84634
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 1074 - 1084[article]On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)
[article]
Titre : On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings Type de document : Article/Communication Auteurs : Madhurima Bandyopadhyay, Auteur ; Jan Van Aardt, Auteur ; Kerry Cawse-Nicholson, Auteur ; Emmett Lentilucci, Auteur Année de publication : 2017 Article en page(s) : pp 123 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] fusion de données
[Termes IGN] image aérienne
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] zone urbaineRésumé : (Auteur) Three-dimensional (3D) data from light detection and ranging (lidar) sensor have proven advantageous in the remote sensing domain for characterization of object structure and dimensions. Fusion-based approaches of lidar and aerial imagery also becoming popular. In this study, aerial color (RGB) imagery, along with co-registered airborne discrete lidar data were used to separate vegetation and buildings from other urban classes/cover-types, as a precursory step towards the assessment of urban forest biomass. Both spectral and structural features such as object height, distribution of surface normals from the lidar, and a novel vegetation metric derived from combined lidar and RGB imagery, referred to as the lidar-infused vegetation index (LDVI) were used in this classification method. The proposed algorithm was tested on different cityscape regions to verify its robustness. Results showed a good separation of buildings and vegetation from other urban classes with on average an overall classification accuracy of 92 percent, with a kappa statistic of 0.85. These results bode well for the operational fusion of lidar and RGB imagery, often flown on the same platform, towards improved characterization of the urban forest and built environments. Numéro de notice : A2017-039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.123 En ligne : https://doi.org/10.14358/PERS.83.2.123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84140
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 123 - 136[article]Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)
Titre : Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas Type de document : Mémoire Auteurs : Cyril Wendl, Auteur ; Arnaud Le Bris , Encadrant Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2017 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de stage, Ecole Polytechnique Fédérale de Lausanne EPFLLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] estimation bayesienne
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation
[Termes IGN] théorie de Dempster-ShaferIndex. décimale : MASTX Mémoires de masters divers Résumé : (auteur) Fusion of very high spatial resolution multispectral images with lower spatial resolution image time series having a higher number of bands can improve land use classification, combining geometric and semantic advantages of both sources. This study presents a workflow to extract the extent of urbanized ground using decision-level fusion and regularization of individual classifications on Sentinel-2 and SPOT-6 satellite images. First, both images are classified individually in five classes, using state-of-the-art supervised classification approaches and Convolutional Neural Networks. Decision-level fusion and regularization are used to combine the spatial and spectral advantages of both sources: First, both sources are merged in order to extract building labels with as high semantic and spatial precision as possible. Second, the building labels are used together with the Sentinel-2 classification as input for a binary classification of the artificialized area; the building labels from the regularization are dilated in order to connect the building objects and a binary classification is derived from the original Sentinel-2 classification before these two separate binary classifications are reintroduced in a fusion and regularization to find the artificialized area. Segmentation of the Sentinel-2 satellite image and majority voting of the object-level classification are also used to refine the contours of the artificialized area. Note de contenu : Introduction
1 - Methodology
2 - Artificialized area
3 - Results
ConclusionNuméro de notice : 21702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Rapport de stage Organisme de stage : MATIS (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90951 Documents numériques
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