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An assessment of the repeatability of automatic forest inventory metrics derived from UAV-borne laser scanning data / Luke Wallace in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)
[article]
Titre : An assessment of the repeatability of automatic forest inventory metrics derived from UAV-borne laser scanning data Type de document : Article/Communication Auteurs : Luke Wallace, Auteur ; Robert Musk, Auteur ; Arko Lucieer, Auteur Année de publication : 2014 Article en page(s) : pp 7160 - 7169 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] semis de pointsRésumé : (Auteur) We assessed the reproducibility of forest inventory metrics derived from an unmanned aerial vehicle (UAV) laser scanning (UAVLS) system. A total of 82 merged point clouds were captured over six 500-m2 plots within a Eucalyptus globulus plantation forest in Tasmania, Australia. Terrain and understory height, together with plot- and tree-level metrics, were extracted from the UAVLS point clouds using automated methods and compared across the multiple point clouds. The results show that measurements of terrain and understory height and plot-level metrics can be reproduced with adequate repeatability for change detection purposes. At the tree level, the high-density data collected by the UAV provided estimates of tree location (mean deviation (MD) of less than 0.48 m) and tree height (MD of 0.35 m) with high precision. This precision is comparable to that of ground-based field measurement techniques. The estimates of crown area and crown volume were found to be dependent on the segmentation routine and, as such, were measured with lower repeatability. The precision of the metrics found within this paper demonstrates the applicability of UAVs as a platform for performing sample-based forest inventories. Numéro de notice : A2014-539 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2308208 En ligne : https://doi.org/10.1109/TGRS.2014.2308208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74156
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 7160 - 7169[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014111A RAB Revue Centre de documentation En réserve L003 Disponible Generating pit-free canopy height models from airborne lidar / Anahita Khosravipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)
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Titre : Generating pit-free canopy height models from airborne lidar Type de document : Article/Communication Auteurs : Anahita Khosravipour, Auteur ; Andrew K. Skidmore, Auteur ; Martin Isenburg, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 863 - 872 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] canopée
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] hauteur des arbres
[Termes IGN] modélisation
[Termes IGN] semis de pointsRésumé : (Auteur)Canopy height models (CHMs) derived from lidar data have been applied to extract forest inventory parameters. However, variations in modeled height cause data pits, which form a challenging problem as they disrupt CHM smoothness, negatively affecting tree detection and subsequent biophysical measurements. These pits appear where laser beams penetrate deeply into a tree crown, hitting a lower branch or ground before producing the first return. In this study, we develop a new algorithm that generates a pit-free CHM raster, by using subsets of the lidar points to close pits. The algorithm operate robustly on high-density lidar data as well as on a thinned lidar dataset. The evaluation involves detecting the finding to those achieved by using a Gaussian smoothed CHM. The results show that our pit-free CHMs derived from high-and low-density lidar data significantly improve the accuracy of tree detection. Numéro de notice : A2014-599 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.9.863 En ligne : https://doi.org/10.14358/PERS.80.9.863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74889
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 9 (September 2014) . - pp 863 - 872[article]A practical target recognition system for close range photogrammetry / M.R. Shortis in Photogrammetric record, vol 29 n° 147 (September - November 2014)
[article]
Titre : A practical target recognition system for close range photogrammetry Type de document : Article/Communication Auteurs : M.R. Shortis, Auteur ; James W. Seager, Auteur Année de publication : 2014 Article en page(s) : pp 337 - 355 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] coordonnées polaires
[Termes IGN] détection automatique
[Termes IGN] détection de cible
[Termes IGN] photogrammétrie terrestreRésumé : (Auteur)This paper presents details of a coded target system that employs polar coordinate transformation and segment matching to automatically recognise and identify targets in digital images. The code system is based on a square surrounding the central circular target and is described at a level of detail that would allow the system to be readily duplicated. Pre-detection processes, developed to improve the success rate under unfavourable conditions, and the tests conducted to validate a correct target match, are described. Finally, the paper includes some examples of the use of the coded targets, drawn from a variety of calibration and measurement applications. Numéro de notice : A2014-92 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12070 Date de publication en ligne : 18/09/2014 En ligne : https://doi.org/10.1111/phor.12070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74081
in Photogrammetric record > vol 29 n° 147 (September - November 2014) . - pp 337 - 355[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)
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Titre : Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery Type de document : Article/Communication Auteurs : L. Yan, Auteur ; X. Niu, Auteur Année de publication : 2014 Article en page(s) : pp 849 - 861 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] angle d'incidence
[Termes IGN] classification Spectral angle mapper
[Termes IGN] détection de cible
[Termes IGN] distance euclidienne
[Termes IGN] image hyperspectrale
[Termes IGN] réduction
[Termes IGN] réflectance spectrale
[Termes IGN] végétationRésumé : In traditional manifold learning of hyperspectral imagery, distances among pixels are defined in terms of Euclidean distance, which is not necessarilly the best choice because of its sensitivity to variations in spectrum magnitudes. Selecting Laplacian Eignemaps (LE) as the test method, this paper studies the effects of distance metric selection in LE and proposes a spectral-angle-based LE method (LE-SA)to be compared against the traditional LE-based on Euclidean distance (LE-ED). Le-SA and LA-ED were applied to two airborne hyperspectral data sets and the dimensionlity-reduced data were quantitatively evalueted. Experimental results demonstrated that LE-SA is able to suppress the variations within the same type of features, such as variations in vegetation and those in illuminations due to shade orientations, and maintain a higher level of overall separability among different features than LE-ED. Further, the potential usage of a single LA-SA or LE-ED band for target detection is discussed. Numéro de notice : A2014-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.9.849 En ligne : https://doi.org/10.14358/PERS.80.9.849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74888
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 9 (September 2014) . - pp 849 - 861[article]Hyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : Hyperspectral remote sensing image subpixel target detection based on supervised metric learning Type de document : Article/Communication Auteurs : Lefei Zhang, Auteur ; Liangpei Zhang, Auteur ; Dacheng Tao, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 4955 - 4965 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification dirigée
[Termes IGN] classification pixellaire
[Termes IGN] détection de cible
[Termes IGN] image hyperspectraleRésumé : (Auteur) The detection and identification of target pixels such as certain minerals and man-made objects from hyperspectral remote sensing images is of great interest for both civilian and military applications. However, due to the restriction in the spatial resolution of most airborne or satellite hyperspectral sensors, the targets often appear as subpixels in the hyperspectral image (HSI). The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong to various land cover classes. In this paper, we propose a novel supervised metric learning (SML) algorithm, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible. The proposed SML algorithm first maximizes the distance between the positive and negative samples by an objective function of the supervised distance maximization. Then, by considering the variety of the background spectral features, we put a similarity propagation constraint into the SML to simultaneously link the target pixels with positive samples, as well as the background pixels with negative samples, which helps to reject false alarms in the target detection. Finally, a manifold smoothness regularization is imposed on the positive samples to preserve their local geometry in the obtained metric. Based on the public data sets of mineral detection in an Airborne Visible/Infrared Imaging Spectrometer image and fabric and vehicle detection in a Hyperspectral Mapper image, quantitative comparisons of several HSI target detection methods, as well as some state-of-the-art metric learning algorithms, were performed. All the experimental results demonstrate the effectiveness of the proposed SML algorithm for hyperspectral target detection. Numéro de notice : A2014-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2286195 En ligne : https://doi.org/10.1109/TGRS.2013.2286195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73971
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 4955 - 4965[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkA local contrast method for small infrared target detection / C.L. Philip Chen in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)PermalinkSuperresolution multitarget parameter estimation in MIMO radar / Kai Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 6 Tome 2 (June 2013)PermalinkDemarcating new boundaries: mapping virtual polycentric communities through social media content / Anthony Stefanidis in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)PermalinkDetecting depolarized targets using a new geometrical perturbation filter / Armando Marino in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)PermalinkStable target detection and coherence estimation in interferometric SAR stacks / P. Guccione in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)PermalinkTracking-Learning-Detection / Zdenek Kalal in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 34 n° 7 (July 2012)PermalinkDétection de bateaux dans les images satellitaires optiques panchromatiques / N. Proia in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)PermalinkSemisupervised one-class support vector machine for classification of remote sensing data / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)PermalinkTraitement de l'image et de la vidéo / Rachid Belaroussi (2010)PermalinkCrack measurement : development, testing and applications of an automatic image-based algorithm / L. Barazetti in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 3 (May - June 2009)PermalinkRadiometric Calibration of LIDAR Intensity With Commercially Available Reference Targets / S. Kaasalainen in IEEE Transactions on geoscience and remote sensing, vol 47 n° 2 (February 2009)PermalinkAnalysis of ground moving objects using SRTM/X-SAR data / S. Suchandt in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 3-4 (December 2006)PermalinkA semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model / X. Niu in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 3-4 (December 2006)PermalinkPhotogrammétrie et muséologie : les sèvres du musée de Rouen / S. Varea in XYZ, n° 108 (septembre - novembre 2006)PermalinkA support vector method for anomaly detection in hyperspectral imagery / Amit Banerjee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)PermalinkDetection of stationary foliage-obscured targets by polarimetric millimeter-wave radar / A.Y. Nashashibi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)PermalinkUsing GPS for augmenting deformation monitoring systems in open pit mines: problems and solutions / J. Bond in Geomatica, vol 59 n° 1 (January 2005)PermalinkDetection of buried targets via active selection of labeled data: Application to sensing subsurface UXO / Y. Zhang in IEEE Transactions on geoscience and remote sensing, vol 42 n° 11 (November 2004)PermalinkLeistungskriterien zur Qualitätskontrolle von Robottachymetern / B. Krikel (2004)PermalinkUnsupervised target detection in hyperspectral images using projection pursuit / S.S. Chiang in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkPermalinkOrtung von eindeutig identifizierbaren Zielen und kodierten Transpondern / D. Hounam (1998)PermalinkThe relative importance of contrast and motion in visual detection / Harold E. Peterson in Human Factors: The Journal of the Human Factors and Ergonomics Society, vol 14 n° 3 (June 1972)Permalink