Descripteur
Termes IGN > imagerie > image spatiale > image satellite > image Quickbird
image QuickbirdVoir aussi |
Documents disponibles dans cette catégorie (126)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery / Biswajeet Pradhan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
[article]
Titre : Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery Type de document : Article/Communication Auteurs : Biswajeet Pradhan, Auteur ; Mustafa Neamah Jebur, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Mahyat Shafapour Tehrany, Auteur Année de publication : 2016 Article en page(s) : pp 1610 - 1622 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] carte thématique
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] effondrement de terrain
[Termes IGN] fusion d'images
[Termes IGN] image Quickbird
[Termes IGN] Malaisie
[Termes IGN] précision des données
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Landslide mapping is indispensable for efficient land use management and planning. Landslide inventory maps must be produced for various purposes, such as to record the landslide magnitude in an area and to examine the distribution, types, and forms of slope failures. The use of this information enables the study of landslide susceptibility, hazard, and risk, as well as of the evolution of landscapes affected by landslides. In tropical countries, precipitation during the monsoon season triggers hundreds of landslides in mountainous regions. The preparation of a landslide inventory in such regions is a challenging task because of rapid vegetation growth. Thus, enhancing the proficiency of landslide mapping using remote sensing skills is a vital task. Various techniques have been examined by researchers. This study uses a robust data fusion technique that integrates high-resolution airborne laser scanning data (LiDAR) with high-resolution QuickBird satellite imagery (2.6-m spatial resolution) to identify landslide locations in Bukit Antarabangsa, Ulu Klang, Malaysia. This idea is applied for the first time to identify landslide locations in an urban environment in tropical areas. A wavelet transform technique was employed to achieve data fusion between LiDAR and QuickBird imagery. An object-oriented classification method was used to differentiate the landslide locations from other land use/covers. The Taguchi technique was employed to optimize the segmentation parameters, whereas the rule-based technique was used for object-based classification. In addition, to assess the impact of fusion in classification and landslide analysis, the rule-based classification method was also applied on original QuickBird data which have not been fused. Landslide locations were detected, and the confusion matrix was used to examine the proficiency and reliability of the results. The achieved overall accuracy and kappa coefficient were 90.06% and 0.84, respectively, for fused data. Mor- over, the acquired producer and user accuracies for landslide class were 95.86% and 95.32%, respectively. Results of the accuracy assessment for QuickBird data before fusion showed 65.65% and 0.59 for overall accuracy and kappa coefficient, respectively. It revealed that fusion made a significant improvement in classification results. The direction of mass movement was recognized by overlaying the final landslide classification map with LiDAR-derived slope and aspect factors. Results from the tested site in a hilly area showed that the proposed method is easy to implement, accurate, and appropriate for landslide mapping in a tropical country, such as Malaysia. Numéro de notice : A2016-127 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2484325 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2484325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80008
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1610 - 1622[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible A feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
[article]
Titre : A feature selection approach for segmentation of very high-resolution satellite images Type de document : Article/Communication Auteurs : Ahmad Izadipour, Auteur ; Behzad Akbari, Auteur ; Barat Mojaradi, Auteur Année de publication : 2016 Article en page(s) : pp 213 - 222 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Geoeye
[Termes IGN] image Quickbird
[Termes IGN] résolution globale (imagerie)
[Termes IGN] segmentation d'imageRésumé : (auteur) Most of the feature selection (FS) methods in the literature determine features that are appropriate only for a given dataset. In contrast, in this paper a FS method that is not dependent to a specific dataset is proposed. In this regard, the effective feature types based on reasonable facts are predefined and appropriate candidate features for each feature type are selected. In proposed method, the features selected from a single labeled image can be used in segmentation of images captured by different satellites with similar spatial resolution. The selected feature types contain spatial and spectral features. The selected features are applied for segmentation of the images captured by QuickBird and GeoEye satellites and obtained results of proposed method are compared with well-known FS methods. Using different evaluation measures, our comparison shows the efficiency of the proposed method in providing better segmentation compared to other FS methods that are presented in this paper. Numéro de notice : A2016-178 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.3.213 En ligne : https://doi.org/10.14358/PERS.82.3.213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80519
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 213 - 222[article]Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis / Frosti Palsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
[article]
Titre : Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis Type de document : Article/Communication Auteurs : Frosti Palsson, Auteur ; Johannes R. Sveinsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2016 Article en page(s) : pp 1247 - 1259 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cohérence des données
[Termes IGN] évaluation
[Termes IGN] fusion d'images
[Termes IGN] image de synthèse
[Termes IGN] image Quickbird
[Termes IGN] image Worldview
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] problème inverse
[Termes IGN] qualité des donnéesRésumé : (Auteur) Pansharpening is the process of fusing a high-resolution panchromatic image and a low-spatial-resolution multispectral image to yield a high-spatial-resolution multispectral image. This is a typical ill-posed inverse problem, and in the past two decades, many methods have been proposed to solve it. Still, there is no general consensus on the best way to quantitatively evaluate the spectral and spatial quality of the fused image. In this paper, we compare the two most widely used and accepted methods for quality evaluation. The first method is the verification of the synthesis property which states that the fused image should be as identical as possible to the multispectral image that the sensor would observe at a higher resolution. This is impossible to verify unless the observed images are spatially degraded so that the original observed multispectral image can be used as reference. The second method is to use metrics that do not use a reference, such as the quality no reference (QNR) metrics. However, there is another property, i.e., the consistency property, which states that the fused image reduced to the resolution of the original multispectral image should be as identical to the original image as possible. This has generally been considered a necessary condition that does not have to imply correct fusion. Using real WorldView-2 and QuickBird data and a total of 18 component substitution and multiresolution analysis methods, we demonstrate that the consistency property can indeed be used to give reliable assessment of the relative performance of pansharpening methods and is superior to using the QNR metrics. Numéro de notice : A2016-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476513 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2476513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80007
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1247 - 1259[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)
[article]
Titre : Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation Type de document : Article/Communication Auteurs : Fateme Ameri, Auteur ; Mohammad Javad Valadan Zoej, Auteur Année de publication : 2015 Article en page(s) : pp 363 - 386 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique d'objets
[Termes IGN] extraction automatique
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] image Ikonos
[Termes IGN] image Quickbird
[Termes IGN] optimisation par essaim de particules
[Termes IGN] réseau routier
[Termes IGN] vectorisationRésumé : (auteur) This paper introduces an innovative automatic road-vectorisation algorithm based on dynamic pixel clustering using particle swarm optimisation. A new cost function is designed to optimise the number and position of road keypoints and is capable of deriving road centrelines without considering geometric, spectral or topological characteristics in the road model. The algorithm is applied to different high-resolution images (IKONOS, QuickBird and aerial photographs) and is evaluated with respect to RMSE, correctness and completeness. Moreover, a new quality parameter is defined to evaluate a “kinking” effect in roads. Extraction of different road shapes with an acceptable precision in both urban and rural environments proves the efficiency of the algorithm in yielding complete road networks. Numéro de notice : A2015-827 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12123 Date de publication en ligne : 15/12/2015 En ligne : https://doi.org/10.1111/phor.12123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79123
in Photogrammetric record > vol 30 n° 152 (December 2015 - February 2016) . - pp 363 - 386[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 106-2015041 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
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
in ISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 62 – 71[article]Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)PermalinkToward evaluating multiscale segmentations of high spatial resolution remote sensing images / Xueliang Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkA fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkBiomass estimation with high resolution satellite images: A case study of Quercus rotundifolia / Adelia M.O. Sousa in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)PermalinkEmploying ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)PermalinkUne approche basée objet combinée avec les classifieurs avancés (SVM, RF, Extra Trees) pour la détection des changements du bâti / Loubna Elmansouri in Revue internationale de géomatique, vol 24 n° 2 (juin - août 2014)PermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)PermalinkFast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty / Xuellang Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)PermalinkA combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery / Bahram Salehi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkComparaison entre les méthodes J-SEG et MeanShift : application sur des données THRS / Rabia Sarah Cheriguene in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)Permalink