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Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)
[article]
Titre : Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image Type de document : Article/Communication Auteurs : Taposh Mollick, Auteur ; MD Golam Azam, Auteur ; Sabrina Karim, Auteur Année de publication : 2023 Article en page(s) : n° 100859 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] Bangladesh
[Termes IGN] classification non dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification pixellaire
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] occupation du sol
[Termes IGN] rendement agricole
[Termes IGN] segmentation d'image
[Termes IGN] utilisation du solRésumé : (auteur) Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective. Numéro de notice : A2023-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rsase.2022.100859 Date de publication en ligne : 22/11/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100859 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102224
in Remote Sensing Applications: Society and Environment, RSASE > vol 29 (January 2023) . - n° 100859[article]Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)
[article]
Titre : Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar Type de document : Article/Communication Auteurs : Andrew W. Whelan, Auteur ; Jeffery B. Cannon, Auteur ; Seth W. Bigelow, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113362 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] diagnostic foliaire
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Géorgie (Etats-Unis)
[Termes IGN] modélisation de la forêt
[Termes IGN] Pinus palustris
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] surface forestière
[Termes IGN] volume en bois
[Termes IGN] voxelRésumé : (auteur) Modeling forest attributes using lidar data has been a useful tool for forest management but the need to correlate lidar to ground-based measurements creates challenges to modeling in diverse forest landscapes. Many lidar models have been based on metrics derived from summarizations of individual lidar returns over sample plot areas, but more recently, metrics based on summarization by volumetric pixel (voxel) have shown promise to better characterize forest structure and distinguish between diverse forest types. Voxel-based metrics may improve characterization of leaf area distribution and horizontal forest structure, which could help create general models of forest attributes applicable in complex landscapes composed of many distinct forest types. We modeled wood volume in longleaf pine woodlands and associated forests to compare how area- and voxel- based lidar metrics predicted wood volume in forest type specific and general predictive models. We created four area-based and six voxel-based metrics to fit models of wood volume using a multiplicative power function. We selected models and compared metric importance using AIC and evaluated model performance using cross-validated mean prediction error. We found that one area-based metric and four voxel-based metrics consistently improved model predictions We suggest that area-based metrics alone may have limitations for characterizing complex forest structure. Area-based summarizes of lidar returns are more heavily influenced by upper canopy returns because lidar returns attenuate below the canopy. By contrast, summarizing lidar returns into a single value per voxel prior to summarization over plots homogenizes point density, giving added weight to sub-canopy returns. Thus voxel-based metrics may be more sensitive to structural variation that may not be adequately captured by area-based metrics alone. This study highlights the potential of voxel-based metrics for characterizing complex forest structure and model generalization capable of accurate forest attribute prediction across diverse forest types. Numéro de notice : A2023-016 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113362 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102150
in Remote sensing of environment > vol 284 (January 2023) . - n° 113362[article]Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)
[article]
Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aire protégée
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SARRésumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253
in Arabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759[article]Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques / J.O. Ondieki in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
[article]
Titre : Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques Type de document : Article/Communication Auteurs : J.O. Ondieki, Auteur ; C.O. Mito, Auteur ; M.I. Kaniu, Auteur Année de publication : 2022 Article en page(s) : n° 102700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de groupement
[Termes IGN] carte thématique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données géologiques
[Termes IGN] image Landsat-OLI
[Termes IGN] Kappa de Cohen
[Termes IGN] Kenya
[Termes IGN] minerai
[Termes IGN] pollution radioactive
[Termes IGN] précision de la classification
[Termes IGN] radioactivité
[Termes IGN] signature spectraleRésumé : (auteur) This study investigates the utility of using remote sensing and geographic information system techniques to accurately infer the presence of radioactive minerals in a typical high background radiation area (HBRA) by analyzing spectral signatures of associated soil, rocks and vegetation. To accomplish this, both unsupervised (K-Means Clustering) and supervised classification techniques based on a maximum likelihood classifier (MLC) were applied to Landsat-8 Imager data from Mrima Hill on Kenya's south coast. The hill is surrounded by dense tropical forest and deeply weathered soils which are rich in Nb, Th, and rare earth elements. Due to high activity concentrations of 232Th (>8 times higher than the world average value for soil), the hill has been designated as a geogenic HBRA. Based on the underlying geological formations, four classifications of vegetation and two classifications of soil/rocks were established and used to indicate the presence of radioactive minerals in the area. Measurements of air-absorbed gamma dose-rates in the area were successfully used to validate these findings. The application of the MLC method on Landsat satellite data shows that this method can be used as a powerful tool to explore and improve radioactive minerals mapping in HBRAs, the overall classification accuracy of Landsat8 OLI data using botanical technique is 80% and the Kappa Coefficient is 0.6. The overall classification accuracy using soil/rocks spectral signatures is 91% and the Kappa Coefficient is 0.7. Finally, the study demonstrated the general utility of remote sensing techniques in radioactive mineral surveys as well as environmental radiological assessments, particularly in resource-constrained settings. Numéro de notice : A2022-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102700 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102700 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99956
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102700[article]Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)
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Titre : Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 Type de document : Article/Communication Auteurs : Nima Pahlevan, Auteur ; Brandon Smith, Auteur ; Krista Alikas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] chlorophylle
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] correction atmosphérique
[Termes IGN] données multisources
[Termes IGN] eaux côtières
[Termes IGN] image Landsat-OLI
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-OLCI
[Termes IGN] matière organique
[Termes IGN] Oregon (Etats-Unis)
[Termes IGN] qualité des eauxRésumé : (auteur) Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model. Numéro de notice : A2022-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112860 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99705
in Remote sensing of environment > vol 270 (March 2022) . - n° 112860[article]Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkMonitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)PermalinkA rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model / Yuqian Dai in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkAssessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])PermalinkCoral 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])PermalinkUncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkIndoor 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)PermalinkApplying 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)PermalinkCan ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])PermalinkClassifying 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)PermalinkApplication of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)PermalinkEvolution 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])PermalinkAilanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkAssessment 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)PermalinkParameter estimation with GNSS-reflectometry and GNSS synthetic aperture techniques / Miguel Angel Ribot Sanfelix (2018)PermalinkA 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)PermalinkEvaluation of multisource data for glacier terrain mapping : a neural net approach / Aparna Shukla in Geocarto international, vol 32 n° 5 (May 2017)PermalinkComparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)PermalinkMRF-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)PermalinkHabitat 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)PermalinkMarkov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image / L. K. Tiwari in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkPermalinkDevelopment and operational analysis of an all-fiber coherent doppler Lidar system for wind sensing and aerosol profiling / Sameh Abdelazim in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkFusion of waveform LiDAR data and hyperspectral imagery for land cover classification / Hongzhou Wang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkIrregular variations in GPS time series by probability and noise analysis / Anna Klos in Survey review, vol 47 n° 342 (May 2015)PermalinkImproved land cover mapping using aerial photographs and satellite images / Katalin Varga in Open geosciences, vol 7 n° 1 (January 2015)PermalinkClassification of submerged aquatic vegetation in Black River using hyperspectral image analysis / Roshan Pande-Chhetri in Geomatica, vol 68 n° 3 (September 2014)PermalinkMaximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas / Michael Schmitt in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkA combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery / Bahram Salehi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkParcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkLa télédétection au service des études urbaines : expansion de la ville de Pondichéry entre 1973 et 2009 / Emilien Kieffer in Géomatique expert, n° 95 (01/11/2013)PermalinkBuilding a forward-mode three-dimensional reflectance model for topographic normalization of High-Resolution (1–5 m) imagery: validation phase in a forested environment / Stéphane Couturier in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkSensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area / Magdalini Pleniou in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkFast error analysis of continuous GNSS observations with missing data / M.S. Bos in Journal of geodesy, vol 87 n° 4 (April 2013)PermalinkComparaison et évaluation de méthodes d'extraction automatique d'objets sur des images optique et radar / Charlotte Benedetto (2013)PermalinkDéveloppement d'outils et de méthodes pour l'estimation de la qualité des résultats de classification / Zhour Najoui (2013)PermalinkEstimation de la qualité des résultats [d'une] classification sous ENVI / Nidal Aburajab (2013)PermalinkPermalinkLiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy / K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)PermalinkApplying six classifiers to airborne hyperspectral imagery for detecting giant reed / C. Yang in Geocarto international, vol 27 n° 5 (August 2012)PermalinkDynamics of coastal landform features along the southern Tamil Nadu of India by using remote sensing and Geographic Information System / P. Mujabar in Geocarto international, vol 27 n° 4 (July 2012)PermalinkA framework for supervised image classification with incomplete training samples / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)PermalinkPermalinkAn assessment of internal neural network parameters affecting image classification accuracy / L. Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 12 (December 2011)PermalinkCartographie des sols hydromorphes de la région des lacs (Côte d'Ivoire) par l'approche du spectral angle mapper (SAM) / G. Zro Bi in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)PermalinkA hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)PermalinkLand cover classification of cloud-contaminated multitemporal high-resolution images / A. Salberg in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)PermalinkRobust Kalman filtering with constraints: a case study for integrated navigation / Y. Yang in Journal of geodesy, vol 84 n° 6 (June 2010)PermalinkTraitement des données de télédétection / Michel-Claude Girard (2010)PermalinkRiparian buffer evaluation, remote sensing for environmental protection at CFB Gagetow / J. Leclerc in GIM international, vol 23 n° 9 (September 2009)PermalinkOptimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)PermalinkA knowledge-based approach to urban feature classification using aerial imagery with Lidar data / M. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 12 (December 2008)PermalinkProbabilities and Multipath : Multipath mitigation techniques using maximum-likelihood principle / Mohamed Sahmoudi in Inside GNSS, vol 3 n° 8 (November - December 2008)PermalinkUsing texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses / F. Aguera in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 6 (November - December 2008)PermalinkApport de deux méthodes de suivi d'évolution de la zone urbaine par imagerie / R. Bouchiha in Revue Française de Photogrammétrie et de Télédétection, n° 190 (Septembre 2008)PermalinkA standardized probability comparison approach for evaluating and combining pixel-based classification procedures / D. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 5 (May 2008)PermalinkUrban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification / Y.O. Ouma in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 3 (May - June 2008)PermalinkArtificial immune-based supervised classifier for land-cover classification / M. Pal in International Journal of Remote Sensing IJRS, vol 29 n° 7 (April 2008)PermalinkMapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data / Z. Zhang in Geocarto international, vol 23 n° 2 (April - May 2008)PermalinkAnalyse spatio-temporelle de l'occupation du sol dans le parc national de Waza entre 1986 et 2001 (Nord Cameroun) / G. Wafo Tabopda in Revue Française de Photogrammétrie et de Télédétection, n° 189 (Mars 2008)PermalinkFast error analysis of continuous GPS observations / M. Bos in Journal of geodesy, vol 82 n° 3 (March 2008)PermalinkMultispectral land use classification using neural networks and support vector machines: one or the other, or both? / B. Dixon in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)PermalinkFusion of support vector machines for classification of multisensor data / Björn Waske in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkLand-cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data / Dong Lu in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)PermalinkModelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery / J. Pasher in Remote sensing of environment, vol 107 n° 3 (12 April 2007)PermalinkComparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)PermalinkMERIS-FR potential for land use-land cover mapping / S. Garcia-Gigorro in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)PermalinkAn experiment using a circular neighborhood to calculate slope gradient from a DEM / X. Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 2 (February 2007)PermalinkMapping salt-marsh vegetation by multispectral and hyperspectral remote sensing / E. Belluco in Remote sensing of environment, vol 105 n° 1 (15/11/2006)PermalinkA novel method for mapping land cover changes: Incorporating time and space with geostatistics / A. Boucher in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)PermalinkComparison of pixel-based and object-oriented image classification approaches: a case study in a coal fire area, Wuda, Inner Mongolia, China / G. Yan in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)PermalinkFuzzy classification: a case study using Landsat TM images in Iran / A.M. Lak in GIM international, vol 20 n° 7 (July 2006)PermalinkIncorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach / A.E. Daniels in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkSome issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkApport de la classification combinée supervisée et non supervisée d'une image Landsat ETM+ à la cartographie géologique de la boutonnière de Kerdous, anti-atlas, Maroc / M. Hakdaoui in Photo interprétation, vol 42 n° 2 (Juin 2006)PermalinkAutomatic building detection using the Dempster-Shafer algorithm / Y.H. Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 4 (April 2006)PermalinkCaractérisation d'un habitat forestier tempéré par télédétection satellitale pour le suivi de populations aviennes : cas des mésanges en forêt de Larivour (Aube, France) / V. Godard in Photo interprétation, vol 41 n° 4 (Novembre 2005)PermalinkOn the relationship between training sample size data dimensionality: Monte Carlo analysis of broadland multi-temporal classification / T.G. Van Niel in Remote sensing of environment, vol 98 n° 4 (30/10/2005)PermalinkTypologie des paysages forestiers du sud du massif de Fontainebleau après la tempête de décembre 1999 / V. Godard in Revue internationale de géomatique, vol 15 n° 3 (septembre – novembre 2005)PermalinkCloud-free satellite image mosaics with regression trees and histogram matching / E.H. Helmert in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 9 (September 2005)PermalinkUtilisation des images satellitaires Spot pour la cartographie des types de peuplements de la forêt de la Mamora (Maroc) / Abderrahman Aafi in Revue Française de Photogrammétrie et de Télédétection, n° 178 (Septembre 2005)PermalinkEstimating and accommodating uncertainty through the soft classification of remote sensing data / M.A. Ibrahim in International Journal of Remote Sensing IJRS, vol 26 n° 14 (July 2005)PermalinkRadial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) / Iphigenia Keramitsoglou in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)PermalinkSatellite remote sensing for detailed landslide inventories using change detection and image fusion / J. Nichol in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)PermalinkA comparison of local variance, fractal dimension, and Moran's index as aids to multispectral image classification / C.W. Emerson in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)PermalinkUse of the Bradley-Terry model to quantify association in remotely sensed images / Alfred Stein in IEEE Transactions on geoscience and remote sensing, vol 43 n° 4 (April 2005)PermalinkL'apport des données du satellite SPOT 5 à l'étude des zones humides en Bretagne nord : application au bassin versant du Jaudy-Guindy-Bizien / S. Saloum in Photo interprétation, vol 41 n° 1 (Mars 2005)PermalinkApport de la polarimétrie radar pour la cartographie thématique en Polynésie française / Cédric Lardeux (2005)PermalinkRoad extraction using SVM and image segmentation / M. Song in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 12 (December 2004)PermalinkApplication d'une méthode de classification orientée objet pour la cartographie de l'occupation du sol : résultats sur ASTER et Landsat ETM / Christina Corbane in Revue Française de Photogrammétrie et de Télédétection, n° 175 (Septembre 2004)PermalinkLe boosting : essai d'une méthode de classification adaptée à la télédétection / David Levrel in Revue internationale de géomatique, vol 14 n° 3 - 4 (septembre 2004 – février 2005)PermalinkSpectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery / Dong Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 9 (September 2004)PermalinkMapping vegetation in a heterogeneous mountain rangeland using Landsat data: an alternative method to define and classify land-cover units / A.M. Cingolani in Remote sensing of environment, vol 92 n° 1 (15 July 2004)PermalinkExamining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case / D. Chen in International Journal of Remote Sensing IJRS, vol 25 n° 11 (June 2004)PermalinkUsing maximum likelihood (ML) and maximum a prior probability (MAP) in iterative self-organizing data (Isodata) / Hassan A. Karimi in Geocarto international, vol 19 n° 1 (March - May 2004)PermalinkImproving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing / C. Tottrup in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)PermalinkMapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data / M.A. Diuk-Wasser in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)PermalinkObject-based classification of remote sensing data for change detection / Volker Walter in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 3-4 (January - June 2004)PermalinkQualité des eaux superficielles et assolement dans le bassin versant du Madon (Lorraine) / F. Masutti (2004)PermalinkTraitement des données de télédétection / Michel-Claude Girard (2004)PermalinkClassification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks / C.S. Murthy in International Journal of Remote Sensing IJRS, vol 24 n° 23 (December 2003)PermalinkImprovements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification / I.A. El-Magd in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)PermalinkMangrove research and coastal ecosystem studies with SPOT-4 HRVIR and TERRA ASTER in the Arabian Gulf / Hideo Saito in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)PermalinkA new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images / P. Lombardo in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)PermalinkBayesian classification by data augmentation / B. Regguzoni in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkComparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change / K.C. Seto in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkA hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkMapping urban extent using satellite radar interferometry / W. Grey in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkSynergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness / M.E. Hodgson in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkMultipath mitigation: how good can it get with new signals ? / L.R. Weill in GPS world, vol 14 n° 6 (June 2003)PermalinkMountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 3 (March 2003)PermalinkTélédétection des changements et SIG / E. Lagabrielle (2003)PermalinkPotential of reflected intensity of airborne laser scanning systems in roadway features identification / Kiyun Yu in Geomatica, vol 56 n° 4 (December 2002)PermalinkLand cover classification models using Shuttle Imaging Radar (SIR-C) data: a case study in New Hampshire, USA / R. Narayanan in Geocarto international, vol 17 n° 3 (September - November 2002)PermalinkA multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)PermalinkIntegration of classification methods for improvement of land-cover map accuracy / XiaoHang Liu in ISPRS Journal of photogrammetry and remote sensing, vol 56 n° 4 (July - August 2002)PermalinkThe utility of very high spatial resolution images to identify urban objects / Anne Puissant in Geocarto international, vol 17 n° 1 (March - May 2002)PermalinkScale and texture in digital image classification / J.S. Ferro in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 1 (January 2002)PermalinkRemote sensing and urban analysis / Jean-Paul Donnay (2001)PermalinkKlassifikation und Interpolation mittels affin invarianter Voronoidiagramme auf der Basis eines Wahrscheinlich- keitsmaßes in großmaßstäbigen Geoinformationssystemen / R. Roschlaub (1999)PermalinkSAR images and ancillary data in crop species interpretation / Leena Matikainen (1998)PermalinkFiltrage du speckle dans les images radar à synthèse d'ouverture polarimétriques et classification supervisée multi-source / Franck Sery (1997)Permalink