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A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data / Hamid Hamraz in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)
[article]
Titre : A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Marco A. Contreras, Auteur ; Jun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 532 - 541 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre (flore)
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuillu
[Termes IGN] houppier
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Kentucky (Etats-Unis)
[Termes IGN] pente
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed-canopy forest with complex terrain and vegetation conditions. The approach identified 94% of dominant and co-dominant trees with a false detection rate of 13%. About 62% of intermediate, overtopped, and dead trees were also detected with a false detection rate of 15%. The overall segmentation accuracy was 77%. Correlations of the segmentation scores of the proposed approach with local terrain and stand metrics was not significant, which is likely an indication of the robustness of the approach as results are not sensitive to the differences in terrain and stand structures. Numéro de notice : A2016-705 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2016.07.006 En ligne : http://dx.doi.org/10.1016/j.jag.2016.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82075
in International journal of applied Earth observation and geoinformation > vol 52 (October 2016) . - pp 532 - 541[article]SAR image change detection based on correlation kernel and multistage extreme learning machine / Lu Jia in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
[article]
Titre : SAR image change detection based on correlation kernel and multistage extreme learning machine Type de document : Article/Communication Auteurs : Lu Jia, Auteur ; Ming Li, Auteur ; Peng Zhang, Auteur ; Yan Wu, Auteur Année de publication : 2016 Article en page(s) : pp 5993 - 6006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] détection de changement
[Termes IGN] détection de contours
[Termes IGN] image radar moirée
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] séparateur à vaste margeRésumé : (auteur) Designing a kernel function with good discriminating ability and a highly application-adaptive kernelized classifier is the key of many kernel methods. However, not many kernel functions combining directly the bitemporal images' information are designed specifically for change detection tasks. In addition, extreme learning machine (ELM) has not found wide applications in change detection tasks, even though it is a potential kernel method possessing outstanding approximation and generalization capabilities as well as great classification accuracy and efficiency. Therefore, an approach relying on a difference correlation kernel (DCK) and a multistage ELM (MS-ELM) is proposed in this paper for synthetic aperture radar (SAR) image change detection. First, a DCK function is constructed specifically for change detection by measuring the “distance” between any two pixels. The DCK function depicts the cross-time similarities between couples of bitemporal image patches at any cyclic shifts with a kernel correlation operation and the high-order spatial distances between two differently located pixels with an algebraic subtraction. The DCK function possesses strong noise immunity and good identification of changed areas simultaneously. Second, an MS-ELM classifier is constructed for obtaining the change detection result. In MS-ELM, the hidden nodes and weights between the hidden and output layers are updated stage by stage by improving the kernel functions that compose them. Each stage of the MS-ELM is a standard kernel-ELM, and the DCK function is utilized in the first stage. The regenerative kernel functions incorporate the output spatial-neighborhood information of the previous stage for enhancing remarkably the MS-ELM's discriminating ability and noise resistance. The converged result at the last stage of MS-ELM is the final change detection result. Experiments on real SAR image change detection demonstrate the effectiveness of the DCK function and the MS-ELM algorithm, particularly its good identification of changed areas and strong robustness against noise in SAR images. Numéro de notice : A2016-865 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2578438 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2578438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82901
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5993 - 6006[article]International benchmarking of the individual tree detection methods for modeling 3-D canopy structure for silviculture and forest ecology using airborne laser scanning / Yunsheng Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
[article]
Titre : International benchmarking of the individual tree detection methods for modeling 3-D canopy structure for silviculture and forest ecology using airborne laser scanning Type de document : Article/Communication Auteurs : Yunsheng Wang, Auteur ; Juha Hyyppä, Auteur ; Xinlian Liang, Auteur ; et al., Auteur ; Clément Mallet , Auteur ; António Ferraz , Auteur Année de publication : 2016 Projets : 1-Pas de projet / Article en page(s) : pp 5011 - 5027 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre (flore)
[Termes IGN] canopée
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] lasergrammétrie
[Termes IGN] longueur d'onde
[Termes IGN] modélisation 3D
[Termes IGN] reconstruction 3D
[Termes IGN] semis de pointsRésumé : (Auteur) Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data. Numéro de notice : A2016-893 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2543225 Date de publication en ligne : 16/06/2016 En ligne : https://helda.helsinki.fi/bitstream/handle/10138/224961/080MML16.pdf;jsessionid= [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83073
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5011 - 5027[article]Documents numériques
Automatic delineation of built-up area at urban block level from topographic maps / Sebastian Muhs in Computers, Environment and Urban Systems, vol 58 (July 2016)
[article]
Titre : Automatic delineation of built-up area at urban block level from topographic maps Type de document : Article/Communication Auteurs : Sebastian Muhs, Auteur ; Hendrik Herold, Auteur ; Gotthard Meinel, Auteur ; Dirk Burghardt, Auteur ; Odette Kretschmer, Auteur Année de publication : 2016 Article en page(s) : pp 71 - 84 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse d'image numérique
[Termes IGN] base de données historiques
[Termes IGN] carte topographique
[Termes IGN] détection du bâti
[Termes IGN] extraction semi-automatique
[Termes IGN] îlot urbain
[Termes IGN] vision par ordinateur
[Termes IGN] zone urbaineRésumé : (auteur) To comprehensively study and better understand urban dynamic processes — such as densification, growth and sprawl, or shrinkage — spatio-temporal databases that allow to track changes of geographic objects like buildings and urban blocks are essential. While comprehensive databases exist for contemporary data, they usually lack a historic dimension. The manual constitution of historic geographic data, be it based on historic maps or aerial images, is a time consuming and laborious process, however. Therefore, we present an approach to semi-automatically extract this data from binary topographic maps with regard to built-up areas at urban block level. The suitability of topographic maps for historic urban analysis has been proven in previous research. To overcome the challenges that are inherent in scanned topographic maps in regard to digital image interpretation we designed a modular process. Among others, these challenges include fused and (multi-)fragmented map objects caused by the overlap of competing content layers in one single binary map. After a preliminary separation of individual map object layers from the map content, the process follows a two-stage top-down approach. At first, the map is organized into street blocks, which after that are re-delineated in regard to built-up area. In doing so, we achieve correctness values ranging from 0.97 to 0.93 for three study sites in Germany. With an increasing number of projects that provide historic topographic maps as georeferenced digital data, our process represents a promising approach to efficiently prepare these historic data for integration into a spatio-temporal database with minimal user intervention. Numéro de notice : A2016-404 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2016.04.001 En ligne : http://dx.doi.org/10.1016/j.compenvurbsys.2016.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81221
in Computers, Environment and Urban Systems > vol 58 (July 2016) . - pp 71 - 84[article]Classifying buildings from point clouds and images / Evangelos Maltezos in GIM international, vol 30 n° 7 (July 2016)
[article]
Titre : Classifying buildings from point clouds and images Type de document : Article/Communication Auteurs : Evangelos Maltezos, Auteur ; Charalabos Ioannnidis, Auteur Année de publication : 2016 Article en page(s) : pp 18 - 21 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement d'images
[Termes IGN] classification dirigée
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] Grèce
[Termes IGN] image optique
[Termes IGN] semis de pointsRésumé : (éditeur) The reconstruction of building outlines provide useful input for land information system. In the city of Kalochory in nethern Greece, a mixed commercial and residential of 33 hectares was selected as a test area to evaluate the classification of buildings. Two data sources were avalaible: airborn Lidar and photographs. These data sources were procesesed to create two separate point clouds.Comparison of the results shows that both data sources can be used for building classification, although more development is needed to improve the robustness of dense image matching. Numéro de notice : A2016-490 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81508
in GIM international > vol 30 n° 7 (July 2016) . - pp 18 - 21[article]Registration-based mapping of aboveground disparities (RMAD) for building detection in off-nadir VHR stereo satellite imagery / Suliman Alaeldin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkAn interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)PermalinkComparison of quality measures for building outline extraction / Markéta Potůčková in Photogrammetric record, vol 31 n° 154 (June - August 2016)PermalinkA multilevel point-cluster-based discriminative feature for ALS point cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (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)PermalinkStreet-side vehicle detection, classification and change detection using mobile laser scanning data / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 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)PermalinkLarge-scale road detection in forested mountainous areas using airborne topographic lidar data / António Ferraz in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)PermalinkMatrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkMulti-criteria, graph-based road centerline vectorization using ordered weighted averaging operators / Fateme Ameri in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)Permalink