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The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
Titre : The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing Type de document : Article/Communication Auteurs : Lihong Su, Auteur ; Y. Huang, Auteur ; James Gibeaut, Auteur ; Longzhuang Li, Auteur Année de publication : 2016 Article en page(s) : pp 859 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] distorsion d'image
[Termes IGN] géoréférencement
[Termes IGN] processeur graphiqueRésumé : (Auteur) Unmanned aerial systems (UAS) have been used as a robust tool for agricultural and environmental applications in recent years. Remote sensing systems based on UAS typically acquire massive hyper-spatial images in its short turnaround. This paper takes advantage of graphics processing unit (GPU) massive parallel computation in order to process the huge data timely and efficiently. More specifically, this paper presents an index array approach for lens distortion correction and geo-referencing. They are the two essential components in UAS hyper-spatial image processing. The index array approach is also capable of parallelizing image file I/O and the orthoimage generation. In addition, this paper presents the dual tiled similarity algorithm for the image co-registration. The index array approach and the dual tiled similarity algorithm were evaluated using two UAS remote sensing datasets of South Padre island shorelines. The results show that this index array approach was able to speed up at least 10 times the lens distortion correction and the geo-referencing relative to the central processing unit (CPU) computation. This dual tiled algorithm could provide 12 times speedup compared with the CPU similarity computation. Numéro de notice : A2016-817 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-016-0253-2 En ligne : http://dx.doi.org/10.1007/s10707-016-0253-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82621
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 859 - 878[article]Automatic rough georeferencing of multiview oblique and vertical aerial image datasets of urban scenes / Styliani Verykokou in Photogrammetric record, vol 31 n° 155 (September - November 2016)
[article]
Titre : Automatic rough georeferencing of multiview oblique and vertical aerial image datasets of urban scenes Type de document : Article/Communication Auteurs : Styliani Verykokou, Auteur ; Charalabos Ioannnidis, Auteur Année de publication : 2016 Article en page(s) : pp 281 - 303 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] automatisation
[Termes IGN] calage
[Termes IGN] géoréférencement
[Termes IGN] image aérienne à axe vertical
[Termes IGN] image aérienne oblique
[Termes IGN] scène urbaine
[Termes IGN] système de référence géodésique
[Termes IGN] visée oblique
[Termes IGN] visée verticaleRésumé : (Auteur) Multi-perspective airborne images that combine oblique and vertical views of the ground have proved to be a valuable source of information for numerous applications requiring a digital representation of the world. In this paper, an automatic methodology for rough georeferencing of large datasets of multiview oblique and vertical aerial images of urban regions without any metadata is proposed. Using feature-based matching combined with robust model fitting and least-squares techniques, the method requires the measurement of a minimum number of points with known coordinates in only one image. The results of this methodology are discussed through the presentation of a developed software suite which identifies the overlapping images, georeferences them, extracts their footprints, subdivides the images into groups based on these footprints and detects the images that cover a specific region. Numéro de notice : A2016-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12156 Date de publication en ligne : 18/09/2016 En ligne : https://doi.org/10.1111/phor.12156 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82246
in Photogrammetric record > vol 31 n° 155 (September - November 2016) . - pp 281 - 303[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016031 RAB Revue Centre de documentation En réserve L003 Disponible Floristic composition and across-track reflectance gradient in Landsat images over Amazonian forests / Javier Muro in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : Floristic composition and across-track reflectance gradient in Landsat images over Amazonian forests Type de document : Article/Communication Auteurs : Javier Muro, Auteur ; Jasper Van Doninck, Auteur ; Hanna Tuomisto, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 361 - 372 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Amazonie
[Termes IGN] angle de visée
[Termes IGN] anisotropie
[Termes IGN] Brésil
[Termes IGN] composition floristique
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] forêt primaire
[Termes IGN] forêt tropicale
[Termes IGN] gradient
[Termes IGN] image Landsat-ETM+
[Termes IGN] Pérou
[Termes IGN] réflectance végétaleRésumé : (Auteur) Remotely sensed image interpretation or classification of tropical forests can be severely hampered by the effects of the bidirectional reflection distribution function (BRDF). Even for narrow swath sensors like Landsat TM/ETM+, the influence of reflectance anisotropy can be sufficiently strong to introduce a cross-track reflectance gradient. If the BRDF could be assumed to be linear for the limited swath of Landsat, it would be possible to remove this gradient during image preprocessing using a simple empirical method. However, the existence of natural gradients in reflectance caused by spatial variation in floristic composition of the forest can restrict the applicability of such simple corrections. Here we use floristic information over Peruvian and Brazilian Amazonia acquired through field surveys, complemented with information from geological maps, to investigate the interaction of real floristic gradients and the effect of reflectance anisotropy on the observed reflectances in Landsat data. In addition, we test the assumption of linearity of the BRDF for a limited swath width, and whether different primary non-inundated forest types are characterized by different magnitudes of the directional reflectance gradient. Our results show that a linear function is adequate to empirically correct for view angle effects, and that the magnitude of the across-track reflectance gradient is independent of floristic composition in the non-inundated forests we studied. This makes a routine correction of view angle effects possible. However, floristic variation complicates the issue, because different forest types have different mean reflectances. This must be taken into account when deriving the correction function in order to avoid eliminating natural gradients. Numéro de notice : A2016-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82503
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 361 - 372[article]Mapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : Mapping of land cover in northern California with simulated hyperspectral satellite imagery Type de document : Article/Communication Auteurs : Matthew L. Clark, Auteur ; Nina E. Kilham, Auteur Année de publication : 2016 Article en page(s) : pp 228 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'occupation du sol
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image hyperspectrale
[Termes IGN] interprétation automatique
[Termes IGN] occupation du sol
[Termes IGN] simulation d'imageRésumé : (Auteur) Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4–21.8% and 3.1–6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9–3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands, woodlands and mixed forests from the classification. This 12-class map had significantly improved accuracy of 85.1% (Kappa 0.83) and most classes had over 70% producer and user accuracies. Finally, we summarized important metrics from the multi-temporal Random Forests to infer the underlying chemical and structural properties that best discriminated our land-cover classes across seasons. Numéro de notice : A2016-783 Affiliation des auteurs : non IGN Autre URL associée : Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82480
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 228 - 245[article]A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images / Anastasios L. Fytsilis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images Type de document : Article/Communication Auteurs : Anastasios L. Fytsilis, Auteur ; Anthony Prokos, Auteur ; Konstantinos D. Koutroumbas, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 165- 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification hybride
[Termes IGN] drone
[Termes IGN] gradient
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] méthodologie
[Termes IGN] orthorectification automatique
[Termes IGN] recalage d'imageRésumé : (Auteur) In this paper a novel integrated hybrid methodology for unsupervised change detection between Unmanned Aerial Vehicle (UAV) and satellite images, which can be utilized in various fields like security applications (e.g. border surveillance) and damage assessment, is proposed. This is a challenging problem mainly due to the difference in geographic coverage and the spatial resolution of the two images, as well as to the acquisition modes which lead to misregistration errors. The methodology consists of the following steps: (a) pre-processing, where the part of the satellite image that corresponds to the UAV image is determined and the UAV image is ortho-rectified using information provided by a Digital Terrain Model, (b) the detection of potential changes, which is based exclusively on intensity and image gradient information, (c) the generation of the region map, where homogeneous regions are produced by the previous potential changes via a seeded region growing algorithm and placed on the region map, and (d) the evaluation of the above regions, in order to characterize them as true changes or not. The methodology has been applied on demanding real datasets with very encouraging results. Finally, its robustness to the misregistration errors is assessed via extensive experimentation. Numéro de notice : A2016-782 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82479
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 165- 186[article]Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkRegression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkSatellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkSemiblind hyperspectral unmixing in the presence of spectral library mismatches / Xiao Fu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkShadow detection and removal in RGB VHR images for land use unsupervised classification / A. Movia in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkTracking the seasonal dynamics of boreal forest photosynthesis using EO-1 hyperion reflectance : sensitivity to structural and illumination effects / Rocío Hernández-Clemente in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkTwo heads are better than one / Brian Curtiss in GEO: Geoconnexion international, vol 15 n° 8 (September 2016)PermalinkDirichlet process based active learning and discovery of unknown classes for hyperspectral image classification / Hao Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkDisaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkQuantitative estimation and validation of the effects of the convergence, bisector elevation, and asymmetry angles on the positioning accuracies of satellite stereo pairs / Jaehoon Jeong in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)PermalinkSimultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing / Paris V. Giampouras in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkSparse output coding for scalable visual recognition / Bin Zhao in International journal of computer vision, vol 119 n° 1 (August 2016)PermalinkPermalinkAssessment of orthoimage and DEM derived from ZY-3 stereo image in Northeastern China / Y. Dong in Survey review, vol 48 n° 349 (July 2016)PermalinkEfficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkEstimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkGeometrical consistency voting strategy for outlier detection in image matching / Luping Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkMeasurement of surface changes in a scaled-down landslide model using high-speed stereo image sequences / Tiantian Feng in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkObject-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery / Mary Pyott Freeman in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkPan-sharpening quality investigation of PLÉIADES-1A images / Mustafa Ozendi in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkRPC-based coregistration of VHR imagery for urban change detection / Shabnam Jabari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkSparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkFusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)PermalinkAn assessment of algorithmic parameters affecting image classification accuracy by random forests / Dee Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)PermalinkAn intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)PermalinkAn interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)PermalinkAutomated bias-compensation approach for pushbroom sensor modeling using digital elevation model / Kwan-Young Oh in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkCorrection of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)PermalinkA spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkSupervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkVector attribute profiles for hyperspectral image classification / Erchan Aptoula in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkAn iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkDeep filter banks for texture recognition, description, and segmentation / Mircea Cimpoi in International journal of computer vision, vol 118 n° 1 (May 2016)PermalinkExploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkICESat/GLAS canopy height sensitivity inferred from Airborne Lidar / Craig Mahoney in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)PermalinkKernel-based domain-invariant feature selection in hyperspectral images for transfer learning / Claudio Persello in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkMidrange geometric interactions for semantic segmentation / Julia Diebold in International journal of computer vision, vol 117 n° 3 (May 2016)PermalinkMultiple morphological component analysis based decomposition for remote sensing image classification / Xiang Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkA novel automatic structural linear feature-based matching method based on new concepts of mathematically-generated-points and lines / Somayeh Yavari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)PermalinkRecent developments in large-scale tie-point matching / Wilfried Hartmann in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)PermalinkThe georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy / Mustafa Erdogan in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkActive-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)PermalinkComparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkComparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover / Bonnie Ruefenacht in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)PermalinkA 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)Permalink