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Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) / Haowei Xu in GPS world, vol 28 n° 3 (March 2017)
[article]
Titre : Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) Type de document : Article/Communication Auteurs : Haowei Xu, Auteur ; Lian Baowang, Auteur ; Charles K. Toth, Auteur ; Dorota Brzezinska, Auteur Année de publication : 2017 Article en page(s) : pp 26 - 32 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification floue
[Termes IGN] couplage GNSS-INS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) Lidar with its superior performance can replace GNSS in the integration solution by providing fixes for the drifting inertial measurement unit (IMU). Tests show its potential for terrain-referenced navigation due to its high accuracy, resolution, update rate and anti-jamming abilities. A novel algorithm uses scanning lidar ranging data and a reference database to calculate the navigation solution of the platform and then further fuse with the inertial navigation system (INS) output data. Numéro de notice : A2017-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85325
in GPS world > vol 28 n° 3 (March 2017) . - pp 26 - 32[article]Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm / Pramod Kumar Konugurthi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)
[article]
Titre : Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm Type de document : Article/Communication Auteurs : Pramod Kumar Konugurthi, Auteur ; Raghavendra Kune, Auteur ; Ravi Nooka, Auteur ; Venkatraman Sarma, Auteur Année de publication : 2016 Article en page(s) : pp 377 - 388 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] algorithme génétique
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] image à très haute résolution
[Termes IGN] orthorectification
[Termes IGN] point d'appui
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) Ortho-rectification of very high resolution imagery from agile platforms using Rigorous Sensor Model / Rational Functional Model is quite challenging and demands a fair amount of interactivity in Ground Control Point (GCP) identification/selection for refining the model and for final product evaluation. The paper proposes achieving complete automation in the ortho-rectification process by eliminating all the interactive components, and incorporating fault tolerance mechanisms within the model to make the process robust and reliable. The key aspects proposed in this paper are: two stage Scale Invariant Feature Transform (SIFT) based matching to obtain a large numbers of checkpoints using much coarser resolution images such as Landsat/ETM+, followed by a GA to select the right combination of minimal GCPS based on minimizing Root Mean Square Error (RMSE) and maximizing the area covered under GCPS, and finally, a decision rule based product evaluation to make the process operate in an "autonomous closed loop mode". The method is generic and has been tested on hundreds of Cartosat-1/2 images, and has achieved above 90% reliability with sub-pixel relative error of reference data. Numéro de notice : A2016-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.5.377 En ligne : http://dx.doi.org/10.14358/PERS.82.5.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81279
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 5 (May 2016) . - pp 377 - 388[article]
Titre : Evaluation of SIFT and SURF for vision based localization Type de document : Article/Communication Auteurs : Xiaozhi Qu , Auteur ; Bahman Soheilian , Auteur ; Emmanuel Habets , Auteur ; Nicolas Paparoditis , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3 Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3 Importance : pp 685 - 692 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compensation locale par faisceaux
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] localisation basée vision
[Termes IGN] point d'intérêt
[Termes IGN] SIFT (algorithme)
[Termes IGN] SURF (algorithme)Résumé : (auteur) Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT. Numéro de notice : C2016-039 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-685-2016 Date de publication en ligne : 10/06/2016 En ligne : https://doi.org/10.5194/isprs-archives-XLI-B3-685-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91851 Documents numériques
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Evaluation of SIFT and SURF ... - pdf éditeurAdobe Acrobat PDF 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]Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation / J.G. Martins in Machine Vision and Applications, vol 26 n° 2-3 (April 2015)
[article]
Titre : Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation Type de document : Article/Communication Auteurs : J.G. Martins, Auteur ; L.S. Oliveira, Auteur ; A.S. Britto Jr, Auteur ; Robert Sabourin, Auteur Année de publication : 2015 Article en page(s) : pp 279 - 293 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] arbre (flore)
[Termes IGN] base de données d'images
[Termes IGN] classificateur
[Termes IGN] données vectorielles
[Termes IGN] reconnaissance d'objets
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texture-based features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior. Numéro de notice : A2015--098 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s00138-015-0659-0 Date de publication en ligne : 29/01/2015 En ligne : http://doi.org/10.1007/s00138-015-0659-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85410
in Machine Vision and Applications > vol 26 n° 2-3 (April 2015) . - pp 279 - 293[article]SAR-SIFT : a SIFT-like algorithm for SAR images / Flora Dellinger in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkA robust image matching method based on optimized BaySAC / Zhizhong Kang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)PermalinkImage matching using SIFT features and relaxation labeling technique—A constraint initializing method for dense stereo matching / Jyoti Joglekar in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)PermalinkSemi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)PermalinkAutomatic orientation and 3D modelling from markerless rock art imagery / J. Lerma in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)PermalinkAppariement entre images de point de vue éloignés par utilisation de carte de profondeur / Narut Soontranon (2013)PermalinkMise en correspondance de points 3D obtenus avec une grande "base-line" / Narut Soontranon (Juin 2013)PermalinkImage matching of satellite data based on quadrilateral control networks / A. Sedaghat in Photogrammetric record, vol 27 n° 140 (December 2012 - February 2013)PermalinkA vector sift detector for interest point detection in hyperspectral imagery / L. Dorado-Munoz in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)PermalinkExtraction of vineyards out of aerial photo-image using texture information / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol I-3 (2012)PermalinkFast and automatic image-based registration of TLS data / M. Weimann in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)PermalinkAutomatic georeferencing of aerial images using stereo high-resolution satellite images / J. Oh in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 11 (November 2011)PermalinkPhotogrammetric processing of low-altitude images acquired by unpiloted aerial vehicles / Y. Zhang in Photogrammetric record, vol 26 n° 134 (June - August 2011)PermalinkPermalinkPermalinkMatching terrestrial images captured by a nomad system to images of a reference database for pose estimation purpose / Arnaud Le Bris (2010)PermalinkUrban area and building detection using SIFT: Keypoints and Graph Theory / B. Simarcek in IEEE Transactions on geoscience and remote sensing, vol 47 n° 4 (April 2009)PermalinkBundle adjustment and pose estimation of images of a multiframe panoramic camera / Bertrand Cannelle (2009)PermalinkSIFT (Scale Invariant Feature Transform) : Un outil pour la mise en correspondance d’images / Arnaud Le Bris (2008)PermalinkExagération des formes basée sur une nouvelle modélisation du linéaire routier / Jean-Georges Affholder in Bulletin d'information de l'Institut géographique national, n° 73 (septembre 2002)Permalink