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Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting / Bufan Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7890 - 7907 Titre : Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting Type de document : Article/Communication Auteurs : Bufan Zhao, Auteur ; Xianghong Hua, Auteur ; Kegen Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7890 - 7907 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] itération [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] regroupement de points [Termes descripteurs IGN] scène intérieure [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points Résumé : (auteur) Indoor scene segmentation based on 3-D laser point cloud is important for rebuilding and classification, especially for permanent building structure. However, the existing segmentation methods mainly focus on the large-scale planar structures but ignore the other sharp structures and details, which would cause accuracy degradation in scene reconstruction. To handle this issue, an iterative Gaussian mapping-based segmentation strategy has been proposed in this article, which goes from rough segmentation to refined one iteratively to decompose the indoor scene into detectable point cloud clusters layer by layer. An improved model fitting algorithm based on the maximum likelihood estimation sampling consensus (MLESAC) algorithm is proposed for refined segmentation, which is called the Prior-MLESAC algorithm, to deal with the extraction of both vertical and nonvertical planar and cylindrical structures. The experimental results demonstrate that planar and cylindrical structures are segmented more completely by the proposed strategy, and more details of the indoor structure are restored than other existing methods. Numéro de notice : A2020-681 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984943 date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984943 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96205 [article]

Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)  

Public [article]  inForests > vol 11 n° 9 (September 2020) . - N° 1016 Titre : Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh Type de document : Article/Communication Auteurs : Mohammad Emran Hasan, Auteur ; Biswajit Nath, Auteur ; A.H.M. Raihan Sarker, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] Bangladesh [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] dégradation de l'environnement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] réserve forestière [Termes descripteurs IGN] réserve naturelle [Termes descripteurs IGN] santé des forêts [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF. Numéro de notice : A2020-752 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091016 date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.3390/f11091016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96432 [article]

Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 11 [01/08/2020] . - pp 214 - 1232 Titre : Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 214 - 1232 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] carte bathymétrique [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification hypercube [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] fond marin [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] récif corallien [Termes descripteurs IGN] réflectance spectrale Résumé : (auteur) Remote sensing has potential in studies of the benthic habitat and extracting the reflectance from the data of multispectral sensors, but traditional image classification techniques cannot provide coral habitat maps with adequate accuracy. This study tested five traditional and three ensemble classification techniques on QuickBird for mapping the benthic composition of coral reefs on the Lang Tengah Island (Malaysia). The common techniques, minimum distance, maximum likelihood, K-nearest neighbour, Fisher and parallelepiped techniques were compared with ensemble classifiers, such as majority voting (MV), simple averaging, and mode combination. The per-class accuracy of the habitat detection improved in the ensemble classifiers; in particular, the MV classifier achieved 95%, 65%, 75% and 95% accuracies for coral, sparse coral, coral rubble and sand, respectively. Ensembles increased the accuracy of the habitat mapping classification by 28%, relative to conventional techniques. Thus, the ensemble techniques can be preferred over the traditional for benthic habitat mapping. Numéro de notice : A2020-459 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1557263 date de publication en ligne : 12/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1557263 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95566 [article]

Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks / Lauwrence V. Stanislawski in International journal of cartography, Vol 6 n° 1 (March 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 1 (March 2020) . - pp 4 - 21 Titre : Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks Type de document : Article/Communication Auteurs : Lauwrence V. Stanislawski, Auteur ; Michael P. Finn, Auteur ; Barbara P. Buttenfield, Auteur Année de publication : 2020 Article en page(s) : pp 4 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme de généralisation [Termes descripteurs IGN] altitude [Termes descripteurs IGN] base de données hydrographiques [Termes descripteurs IGN] cartographie des flux [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] cours d'eau [Termes descripteurs IGN] drainage [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] pente [Termes descripteurs IGN] perméabilité du sol [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] réseau hydrographique [Termes descripteurs IGN] ruissellement [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] traitement automatique de données [Vedettes matières IGN] Généralisation Résumé : (auteur) Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and characterization of regional conditions have not been formally defined nor statistically validated. This paper undertakes a systematic classification of landscape types in the conterminous United States to spatially subset the country into workable units, in preparation for systematic tailoring of generalization workflows that preserve hydrographic characteristics. The classification is based upon elevation, standard deviation of elevation, slope, runoff, drainage and bedrock density, soil and bedrock permeability, area of inland surface water, infiltration-excess of overland flow, and a base flow index. A seven class solution shows low misclassification rates except in areas of high landscape diversity such as the Appalachians, Rocky Mountains, and Western coastal regions. Numéro de notice : A2020-070 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2018.1443759 date de publication en ligne : 20/03/2018 En ligne : https://doi.org/10.1080/23729333.2018.1443759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94632 [article]

Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 13 [15/10/2019] . - pp 1514 - 1529 Titre : Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco Type de document : Article/Communication Auteurs : Ali Aydda, Auteur ; Omar F. Althuwaynee, Auteur ; Ahmed Algouti, Auteur ; Abdellah Algouti, Auteur Année de publication : 2019 Article en page(s) : pp 1514 - 1529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte géomorphologique [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] dune [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] littoral [Termes descripteurs IGN] Maroc [Termes descripteurs IGN] sable [Termes descripteurs IGN] vent de sable Résumé : (auteur) The study anticipated to understand sand encroachment evolution through analysis of sand contribution across space and time using remote sensing in Laâyoune-Tarfaya basin, Morocco, over the period from 1987 to 2011. The assessment based on supervised classifications of Landsat imagery orthorectified data, using Maximum Likelihood (ML), Minimum Distance (MD) and Support Vector Machine (SVM) classifiers. In order to ameliorate the information, principal components analysis (PCA) and co-occurrence measurement algorithm were used for choosing bands and data transformation. Images differencing was applied on image pairs derived from classification to analyze sand encroachment evolution. All classifiers present enhanced performances, and revealed that area covered by sand was increased by 7%, 4.66% and 4.59% for ML, MD and SVM, respectively. Consequently, images differencing results confirmed that sand material increasing arise not only from coastal area contribution but also mostly from erosion of complicated sand dunes exist in the middle part of the studied area. Evaluating of the presented phenomenon dimensions and its consequences are extremely important to increase the local authorities awareness and mainly for avoiding or minimizing the consequences of the future sand dunes threats. Numéro de notice : A2019-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1493154 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1493154 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93820 [article]

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