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Parallel performance of typical algorithms in remote sensing-based mapping on a multi-core computer / Jinghui Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 5 (May 2015) . - pp 373 - 385 Titre : Parallel performance of typical algorithms in remote sensing-based mapping on a multi-core computer Type de document : Article/Communication Auteurs : Jinghui Yang, Auteur ; Jixian Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 373 - 385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] processeur multicoeur [Termes IGN] traitement d'image [Termes IGN] traitement parallèle Résumé : (auteur) Typical algorithms in remote sensing-based mapping, such as geometric correction, image fusion, image mosaic, and automatic DEM extractions, are data- and computation-intensive; processing on multi-core computers can improve their performance. Therefore, parallel computing methods that can fully leverage state-of-the-art hardware platforms and that can be easily adapted to these algorithms are required. In this paper, a method with high parallelism is adopted. The method integrates a recursive procedure with a parallel mechanism that is capable of concurrently processing multiple blocks on multiple cores. The parallel experiments of five categories of typical algorithms on two multi-core computers with Windows and Linux operating systems, respectively, were fulfilled. The experimental results show that although the gains of parallel performance vary for different algorithms, the processing performance achieved on multi-core computers is significantly improved. The best case on a computer with two CPUs is able to perform the DEM extractions up to 13.6 times faster than serial execution. According to these experiments, the factors influencing parallel performance on a multi-core computer are discussed. Numéro de notice : A2015-972 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.14358/PERS.81.5.373 En ligne : https://doi.org/10.14358/PERS.81.5.373 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80042 [article]

Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification / Jixian Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 81 (July 2013) . - pp 44 - 59 Titre : Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification Type de document : Article/Communication Auteurs : Jixian Zhang, Auteur ; Xiangguo Lin, Auteur Année de publication : 2013 Article en page(s) : pp 44 - 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] densification [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage du signal [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] traitement automatique de données [Termes IGN] Triangulated Irregular Network Résumé : (Auteur) Progressive TIN densification (PTD) is one of the classic methods for filtering airborne LiDAR point clouds. However, it may fail to preserve ground measurements in areas with steep terrain. A method is proposed to improve the PTD using a point cloud segmentation method, namely segmentation using smoothness constraint (SUSC). The classic PTD has two core steps. The first is selecting seed points and constructing the initial TIN. The second is an iterative densification of the TIN. Our main improvement is embedding the SUSC between these two steps. Specifically, after selecting the lowest points in each grid cell as initial ground seed points, SUSC is employed to expand the set of ground seed points as many as possible, as this can identify more ground seed points for the subsequent densification of the TIN-based terrain model. Seven datasets of ISPRS Working Group III/3 are utilized to test our proposed algorithm and the classic PTD. Experimental results suggest that, compared with the PTD, the proposed method is capable of preserving discontinuities of landscapes and reducing the omission errors and total errors by approximately 10% and 6% respectively, which would significantly decrease the cost of the manual operation required for correcting the result in post-processing. Numéro de notice : A2013-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32526 [article]

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Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas / Guijun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 77 (March 2013) . - pp 79 - 93 Titre : Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas Type de document : Article/Communication Auteurs : Guijun Yang, Auteur ; Ruiliang Pu, Auteur ; Jixian Zhang, Auteur ; Chunjiang Zhao, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 79 - 93 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] couvert végétal [Termes IGN] effet atmosphérique [Termes IGN] image Landsat-TM [Termes IGN] montagne [Termes IGN] Pékin (Chine) [Termes IGN] variabilité [Termes IGN] variation saisonnière Résumé : (Auteur) Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be beneficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensing has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates the use of a revised physically based (RPB) model to correct both atmospheric and terrain-caused illumination effects on Landsat images, an improved vegetation index (VI)-based technique for estimating the FVC, and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shape metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variability of FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB–NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground-based FVC observations and FVC estimated through remote sensing technology (R2 = 0.8527, RMSE = 0.0851); and (c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its maximum in May and June in the study area. Numéro de notice : A2013-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.11.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32255 [article]

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Remote sensing research issues of the national land use change program of China / Jixian Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 6 (November-December 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 6 (November-December 2007) . - pp 461 - 472 Titre : Remote sensing research issues of the national land use change program of China Type de document : Article/Communication Auteurs : Jixian Zhang, Auteur ; Y. Zhang, Auteur Année de publication : 2007 Article en page(s) : pp 461 - 472 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture [Termes IGN] aménagement du territoire [Termes IGN] Chine [Termes IGN] détection de changement [Termes IGN] développement agricole [Termes IGN] développement durable [Termes IGN] gestion de production [Termes IGN] traitement d'image [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (Auteur) In 1999, the Ministry of Land and Resources (MLR) of China launched the National Land Use Change Program especially to monitor the scale and distribution of urban expansion and the decrease in cultivated land through remote sensing technology. This Program has been carried out annually and continuously for seven years since then and played an important role in the policy-making of MLR about land management and planning. This paper gives an overview about this Program and discusses several research issues. First, the remote sensing data sources and other ancillary data used in this Program are presented. The approaches for image preprocessing, i.e. radiometric normalization, image geometric rectification and image fusion are then introduced with an emphasis on the algorithm development for image registration. Second, land use change detection technique is the most critical and complex aspect of the Program. The methodologies for change detection using either bi-temporal image pair or one existing land use map and one remotely sensed image are detailed. Third, since the data of land use changes derived from remote sensing will be operationally used for local and central government, field validation and accuracy assessment are crucial to ensure the reliability of change detection results. The strategy of field work and the resulting accuracy evaluations is presented. The land use and change information derived from remotely sensed data has wide applications for land management, including land use database updating, verification of land use planning and monitoring of national high-tech parks. Last, suggestions on how to make full use of the images and change detection result, to improve the consistency of land use classification and to develop change detection algorithms for diverse and complex remote sensing data are given. Copyright ISPRS Numéro de notice : A2007-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.07.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.07.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28893 [article]

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