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Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [15/06/2021] . - pp 1217 - 1235 Titre : Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1217 - 1235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification bayesienne [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification pixellaire [Termes IGN] fond marin [Termes IGN] Google Earth [Termes IGN] habitat d'espèce [Termes IGN] image Quickbird [Termes IGN] Malaisie [Termes IGN] précision infrapixellaire [Termes IGN] récif corallien Résumé : (auteur) This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping. Numéro de notice : A2021-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637466 date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97803 [article]

Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)  

Public [article]  inApplied geomatics > vol 13 n° 2 (June 2021) . - pp 261 - 275 Titre : Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery Type de document : Article/Communication Auteurs : Mahmoud Salah, Auteur Année de publication : 2021 Article en page(s) : pp 261 - 275 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] appariement d'histogramme [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de changement [Termes IGN] géoréférencement [Termes IGN] Giza [Termes IGN] image à très haute résolution [Termes IGN] image Geoeye [Termes IGN] image multitemporelle [Termes IGN] incertitude des données [Termes IGN] méthode robuste [Termes IGN] modèle de Markov caché [Termes IGN] occupation du sol [Termes IGN] réseau neuronal artificiel [Termes IGN] utilisation du sol Résumé : (auteur) Robust approaches for image change detection (ICD) are essential for a range of large-scale applications. However, the uncertainties involved in such approaches have not been fully addressed. To investigate this problem, this paper proposes a new approach for change detection from multi-temporal very high resolution (VHR) satellite imagery based on uncertainty detection and management. First, two GeoEye-1 images of Giza urban area (Egypt), acquired in 2009 and 2019, have been geographically co-registered and their histograms have been matched. Second, a set of feature attributes have been generated from the co-registered images. Third, the support vector machine (SVM) algorithm has been adopted to classify the data into four classes: building, tree, road, and ground. In this regard, the co-registered images along with the generated attributes have been applied as input data for the SVM to calculate the probability of each pixel belonging to each class. After that, the probability images for both epochs have been compared to model the uncertainty of changes. The uncertainty places are then evaluated to estimate their likelihood of being change or no change. Finally, the obtained results have been compared with manually digitized change detection map. Compared with using the widely used post-classification comparison (PCC) approach, the results suggest that (1) the proposed method has improved the overall accuracy of change detection by 13%; (2) the class-accuracies have been improved by 35.63%; and (3) the achieved accuracies for the proposed approach are less variable. Whereas the standard deviation (SD) of the accuracies obtained for the proposed approach is 6.80, the SD of those obtained for the PCC approach is 35.50. Numéro de notice : A2021-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00346-z date de publication en ligne : 28/10/2020 En ligne : https://doi.org/10.1007/s12518-020-00346-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97737 [article]

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 IGN] classification par maximum de vraisemblance [Termes IGN] données lidar [Termes IGN] itération [Termes IGN] modélisation 3D [Termes IGN] processus gaussien [Termes IGN] reconstruction 3D du bâti [Termes IGN] regroupement de points [Termes IGN] scène intérieure [Termes IGN] segmentation [Termes 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 IGN] automate cellulaire [Termes IGN] Bangladesh [Termes IGN] classification par maximum de vraisemblance [Termes IGN] couvert forestier [Termes IGN] déboisement [Termes IGN] dégradation de l'environnement [Termes IGN] détection de changement [Termes IGN] gestion forestière durable [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] mangrove [Termes IGN] modèle de Markov [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] réserve forestière [Termes IGN] réserve naturelle [Termes IGN] santé des forêts [Termes IGN] série temporelle [Termes 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 IGN] biodiversité [Termes IGN] carte bathymétrique [Termes IGN] Chine, mer de [Termes IGN] classification barycentrique [Termes IGN] classification hypercube [Termes IGN] classification par maximum de vraisemblance [Termes IGN] distribution de Fisher [Termes IGN] fond marin [Termes IGN] image multibande [Termes IGN] Malaisie [Termes IGN] précision de la classification [Termes IGN] récif corallien [Termes 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)  

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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])  

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Ailanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

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Assessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria / Christopher Ifechukwude Chima in Geocarto international, vol 33 n° 9 (September 2018)  

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Parameter estimation with GNSS-reflectometry and GNSS synthetic aperture techniques / Miguel Angel Ribot Sanfelix (2018)  

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A relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)  

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Evaluation of multisource data for glacier terrain mapping : a neural net approach / Aparna Shukla in Geocarto international, vol 32 n° 5 (May 2017)  

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Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)    

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MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images / Ilias Grinias in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)  

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Habitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery / Guy W. Jeter Jr in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)  

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