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Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)
[article]
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] Egypte
[Termes IGN] géoréférencement
[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 solRésumé : (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
in Applied geomatics > vol 13 n° 2 (June 2021) . - pp 261 - 275[article]Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)
[article]
Titre : Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery Type de document : Article/Communication Auteurs : Neema Nicodemus Lyimo, Auteur ; Fang Luo, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur Année de publication : 2021 Article en page(s) : pp 339-348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] appariement d'images
[Termes IGN] distance euclidienne
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données hétérogènes
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] données ouvertes
[Termes IGN] image Landsat
[Termes IGN] incertitude des données
[Termes IGN] jeu de données localisées
[Termes IGN] qualité des données
[Termes IGN] système à base de connaissances
[Termes IGN] zone urbaineRésumé : (Auteur) Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification. Numéro de notice : A2021-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.339 Date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.339 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97695
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 339-348[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021051 SL Revue Centre de documentation Revues en salle Disponible Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties / NIca Huber in Ecological Applications, vol 31 ([01/02/2021])
[article]
Titre : Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties Type de document : Article/Communication Auteurs : NIca Huber, Auteur ; Harald Bugmann, Auteur Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] forêt alpestre
[Termes IGN] incertitude des données
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] modélisation de la forêt
[Termes IGN] Suisse
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The increasing impacts of climate change on forest ecosystems have triggered multiple model-based impact assessments for the future, which typically focused either on a small number of stand-scale case studies or on large scale analyses (i.e., continental to global). Therefore, substantial uncertainty remains regarding the local impacts over large areas (i.e., regions to countries), which is particularly problematic for forest management. We provide a comprehensive, high-resolution assessment of the climate change sensitivity of managed Swiss forests (~10,000 km2), which cover a wide range of environmental conditions. We used a dynamic vegetation model to project the development of typical forest stands derived from a stratification of the Third National Forest Inventory until the end of the 22nd century. Two types of simulations were conducted: one limited to using the extant local species, the other enabling immigration of potentially more climate-adapted species. Moreover, to assess the robustness of our projections, we quantified and decomposed the uncertainty in model projections resulting from the following sources: (1) climate change scenarios, (2) local site conditions, and (3) the dynamic vegetation model itself (i.e., represented by a set of model versions), an aspect hitherto rarely taken into account. The simulations showed substantial changes in basal area and species composition, with dissimilar sensitivity to climate change across and within elevation zones. Higher-elevation stands generally profited from increased temperature, but soil conditions strongly modulated this response. Low-elevation stands were increasingly subject to drought, with strong negative impacts on forest growth. Furthermore, current stand structure had a strong effect on the simulated response. The admixture of drought-tolerant species was found advisable across all elevations to mitigate future adverse climate-induced effects. The largest uncertainty in model projections was associated with climate change scenarios. Uncertainty induced by the model version was generally largest where overall simulated climate change impacts were small, thus corroborating the utility of the model for making projections into the future. Yet, the large influence of both site conditions and the model version on some of the projections indicates that uncertainty sources other than climate change scenarios need to be considered in climate change impact assessments. Numéro de notice : A2021-312 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1002/eap.2313 Date de publication en ligne : 25/02/2021 En ligne : https://doi.org/10.1002/eap.2313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97811
in Ecological Applications > vol 31 [01/02/2021][article]Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin / Luis Felipe Galizia in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
[article]
Titre : Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin Type de document : Article/Communication Auteurs : Luis Felipe Galizia, Auteur ; Thomas Curt, Auteur ; Renaud Barbero, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 73 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin méditerranéen
[Termes IGN] cartographie des risques
[Termes IGN] exactitude des données
[Termes IGN] image Terra-MODIS
[Termes IGN] incendie
[Termes IGN] incertitude des données
[Termes IGN] jeu de données localiséesRésumé : (auteur) Recently, many remote-sensing datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire datasets. Here, we compared three state-of-the-art remote-sensing datasets (RSDs; Fire Atlas, FRY, and GlobFire) with a harmonized ground-based dataset (GBD) compiled by fire agencies monitoring systems across the southwestern Mediterranean Basin (2005–2015). We assessed the agreement between the RSDs and the GBD with respect to both burned area (BA) and number of fires (NF). RSDs and the GBD were aggregated at monthly and 0.25∘ resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that all datasets were highly correlated in terms of monthly BA and NF, but RSDs severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. The agreement between RSDs and the GBD was strongly dependent on individual fire size and strengthened when increasing the fire size threshold, with fires > 100 ha denoting a higher correlation and much lower error (BA 10 %; NF 35 %). The agreement was also higher during the warm season (May to October) in particular across the regions with greater fire activity such as the northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance with a lower relative error, although uncertainty in the gridded BA product largely outpaced uncertainties across the RSDs. Overall, our findings suggest a reasonable agreement between RSDs and the GBD for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales. Numéro de notice : A2021-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/nhess-21-73-2021 Date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.5194/nhess-21-73-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96995
in Natural Hazards and Earth System Sciences > vol 21 n° 1 (January 2021) . - pp 73 - 86[article]Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery Type de document : Article/Communication Auteurs : Nazanin Asadi, Auteur ; K. Andrea Scott, Auteur ; Alexander S. Komarov, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 247 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] assimilation des données
[Termes IGN] classification pixellaire
[Termes IGN] glace de mer
[Termes IGN] image radar moirée
[Termes IGN] incertitude des données
[Termes IGN] modèle d'incertitude
[Termes IGN] Perceptron multicouche
[Termes IGN] pondération
[Termes IGN] précision de la classification
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular importance. Although several automated approaches have been proposed, none look at the impact of including an estimate of uncertainty of the model parameters and input features on the classification output. This article uses an established database of SAR image features to train a multilayer perceptron (MLP) neural network to classify pixel locations as either ice, water, or unknown. The classification accuracies are benchmarked using a recently developed logistic regression approach for the same database. The two methods are found to be comparable. The MLP approach is then enhanced to allow uncertainty to be estimated at each pixel location. Following methods proposed in the deep learning community, two kinds of uncertainty are considered. The first, epistemic uncertainty, is that due to uncertainty in the MLP weights. The second kind of uncertainty, aleatoric uncertainty, is that which cannot be explained by the model, and is therefore associated with the input data. It is found that including these uncertainties in the MLP models reduces their accuracies slightly, but also reduces misclassification rates. This is of particular importance for data assimilation applications, where misclassifications could severely degrade the analysis. Numéro de notice : A2021-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992454 Date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992454 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96735
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 247 - 259[article]Active and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkMapping uncertain geographical attributes: incorporating robustness into choropleth classification design / Wangshu Mu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)PermalinkCombined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkUncertainty of forested wetland maps derived from aerial photography / Stephen P. Prisley in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 10 (October 2020)PermalinkImproved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkOptimal lowest astronomical tide estimation using maximum likelihood estimator with multiple ocean models hybridization / Mohammed El-Diasty in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)PermalinkWavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system / Elahe S. Abdolkarimi in GPS solutions, vol 24 n° 2 (April 2020)PermalinkVolcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkUncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces / James A. Thompson in Remote sensing, vol 12 n° 1 (January 2020)PermalinkSpatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model / Mariana Madruga de bruto in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)Permalink