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Termes IGN > imagerie
imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery / Komeil Rokni in Geodesy and cartography, vol 49 n° 1 (January 2023)
[article]
Titre : Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery Type de document : Article/Communication Auteurs : Komeil Rokni, Auteur Année de publication : 2023 Article en page(s) : pp 12 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Gram-Schmidt
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] Kappa de Cohen
[Termes IGN] matrice de confusion
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] précision de la classificationRésumé : (auteur) Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose. Numéro de notice : A2023-142 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3846/gac.2023.15308 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.3846/gac.2023.15308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102712
in Geodesy and cartography > vol 49 n° 1 (January 2023) . - pp 12 - 18[article]Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach / Shenglong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)
[article]
Titre : Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach Type de document : Article/Communication Auteurs : Shenglong Chen, Auteur ; Yoshiki Ogawa, Auteur ; Chenbo Zhao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 129 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur (variable spectrale)
[Termes IGN] détection du bâti
[Termes IGN] distribution de Gauss
[Termes IGN] image à haute résolution
[Termes IGN] mosaïquage d'images
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Building footprint is a primary dataset of an urban geographic information system (GIS) database. Therefore, it is essential to establish a robust and automated framework for large-scale building extraction. However, the characteristic of remote sensing images complicates the application of the instance segmentation method based on the Mask R-CNN model, which ought to be improved toward extracting and fusing multi-scale features. Moreover, open-source satellite image datasets with wider spatial coverage and temporal resolution than high-resolution images may exhibit different coloration and resolution. This study proposes a large-scale building extraction framework based on super-resolution (SR) and instance segmentation using a relatively lower-resolution (>0.6 m) open-sourced dataset. The framework comprises four steps: color normalization and image super-resolution, scene classification, building extraction, and scene mosaicking. We took Hyogo Prefecture, Japan (19,187 km2) as a test area and extracted 1,726,006 (29.12 km2) of the 3,301,488 buildings (32.46 km2), where the number of buildings and footprint area increased by 3.0 % and 5.0 % respectively. The result indicated that the color normalization and image super-resolution could improve the visual quality of open-source satellite images and contribute to building extraction accuracy. Moreover, the improved Mask R-CNN based on Multi-Path Vision Transformer (MPViT) backbone achieved F1 scores of 0.71, 0.70, 0.81, and 0.67 for non-built-up, rural, suburban, and urban areas, respectively, which is better than those of the baseline model and other mainstream instance segmentation approaches. This study demonstrates the potential of acquiring acceptable building footprint maps from open-source satellite images, which has significant practical implications. Numéro de notice : A2023-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.11.006 Date de publication en ligne : 30/11/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.11.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102214
in ISPRS Journal of photogrammetry and remote sensing > vol 195 (January 2023) . - pp 129 - 152[article]
Titre : Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans Type de document : Article/Communication Auteurs : Romain Loiseau , Auteur ; Elliot Vincent, Auteur ; Mathieu Aubry, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Importance : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] information complexe
[Termes IGN] scène 3D
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) We propose an unsupervised method for parsing large 3D scans of real-world scenes into interpretable parts. Our goal is to provide a practical tool for analyzing 3D scenes with unique characteristics in the context of aerial surveying and mapping, without relying on application-specific user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical shapes. Our model provides an interpretable reconstruction of complex scenes and leads to relevant instance and semantic segmentations. To demonstrate the usefulness of our results, we introduce a novel dataset of seven diverse aerial LiDAR scans. We show that our method outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our method offers significant advantage over existing approaches, as it does not require any manual annotations, making it a practical and efficient tool for 3D scene analysis. Our code and dataset are available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parser Numéro de notice : P2023-005 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans En ligne : https://hal.science/hal-04135416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103347 A machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)
[article]
Titre : A machine learning method for Arctic lakes detection in the permafrost areas of Siberia Type de document : Article/Communication Auteurs : Piotr Janiec, Auteur ; Jakub Nowosad, Auteur ; Sbigniew Zwoliński, Auteur Année de publication : 2023 Article en page(s) : n° 2163923 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Arctique
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] lac glaciaire
[Termes IGN] MERIT
[Termes IGN] modèle numérique de surface
[Termes IGN] pergélisol
[Termes IGN] Short Waves InfraRed
[Termes IGN] SibérieRésumé : (auteur) Thermokarst lakes are the main components of the vast Arctic and subarctic landscapes. These lakes can serve as geoindicators of permafrost degradation; therefore, proper lake distribution assessment methods are necessary. In this study, we compared four machine learning methods to improve existing lake detection systems. The northern part of Yakutia was selected as the study area owing to its complex environment. We used data from Landsat 8 and spectral indices to take into account the spectral characteristics of the lakes, and MERIT DEM data to take into account the topography. The lowest accuracy was found for the classification and regression trees (CART) method (overall accuracy = 81%). On the other hand, the random forests (RF) classification provided the best results (overall accuracy = 92%), and only this classification coped well in all problematic areas, such as shaded and humid areas, near steep slopes, burn scars, and rivers. The altitude and bands SWIR1 (Short wave infrared 1), SWIR2 (Short wave infrared 2), and Green were the most important. Spectral indices did not have significant impact on the classification results in the specific conditions of the thermokarst lakes environment. 17,700 lakes were identified with the total area of 271.43 km2. Numéro de notice : A2023-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2163923 Date de publication en ligne : 19/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2163923 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103156
in European journal of remote sensing > vol 56 n° 1 (2023) . - n° 2163923[article]Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
[article]
Titre : Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami Type de document : Article/Communication Auteurs : Riantini Virtriana, Auteur ; Agung Budi Harto, Auteur ; Fiza Wira Atmaja, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 28 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données d'images
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dommage matériel
[Termes IGN] données Copernicus
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] Indonésie
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] tsunamiRésumé : (auteur) In Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas. Numéro de notice : A2023-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2147455 Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1080/19475705.2022.2147455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102307
in Geomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 28 - 51[article]Mapping active paddy rice area over monsoon asia using time-series Sentinel-2 images in Google earth engine : a case study over lower gangetic plain / Arabinda Maiti in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkA method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkMulti-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkA new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing / Yali Zhang in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkProduction of orthophoto map using mobile photogrammetry and comparative assessment of cost and accuracy with satellite imagery for corridor mapping: a case study in Manesar, Haryana, India / Manuj Dev in Annals of GIS, vol 29 n° 1 (January 2023)PermalinkPSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)PermalinkDes relevés sur mesure pour la sentinelle des Pyrénées / Marielle Mayo in Géomètre, n° 2209 (janvier 2023)PermalinkRemote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia / Lifan Ji in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkSediment yield estimation in GIS environment using RUSLE and SDR model in Southern Ethiopia / Dawit Kanito in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkSensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)Permalink