Descripteur
Documents disponibles dans cette catégorie (2578)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])
[article]
Titre : A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas Type de document : Article/Communication Auteurs : Hossein Pourazar, Auteur ; Farhad Samadzadegan, Auteur ; Farzaneh Dadrass Javan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6695 - 6712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement des données
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotoplan numérique
[Termes IGN] zone urbaineRésumé : (auteur) In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set. Numéro de notice : A2022-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959655 Date de publication en ligne : 04/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101741
in Geocarto international > vol 37 n° 23 [15/10/2022] . - pp 6695 - 6712[article]Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])
[article]
Titre : Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information Type de document : Article/Communication Auteurs : Ozlem Akar, Auteur ; Esra Tunc Gormus, Auteur Année de publication : 2022 Article en page(s) : pp 6643 - 6670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte de la végétation
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettes
[Termes IGN] TurquieRésumé : (auteur) Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability to see small details on the field. As this technology has newly developed, most of the image processing methods are for the medium resolution sensors and they are not capable of dealing with high resolution images. Therefore, in this study a new framework is proposed to improve the classification accuracy of land use/cover mapping applications and to achieve a greater reliability in the process of mapping land use map using high resolution hyperspectral image data. In order to achieve it, spatial information is incorporated together with spectral information by exploiting feature extraction methods like Grey Level Co-occurrence Matrix (GLCM), Gabor and Morphological Attribute Profile (MAP) on dimensionally reduced image with highest accuracy. Then, machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM) are used to investigate the contribution of texture information in the classification of high resolution hyperspectral images. In addition to that, further analysis is conducted with object based RF classification to investigate the contribution of contextual information. Finally, overall accuracy, producer’s/user’s accuracy, the quantity and allocation based disagreements and location and quantity based kappa agreements are calculated together with McNemar tests for the accuracy assessment. According to our results, proposed framework which incorporates Gabor texture information and exploits Discrete Wavelet Transform based dimensionality reduction method increase the overall classification accuracy up to 9%. Amongst individual classes, Gabor features boosted classification accuracies of all the classes (soil, road, vegetation, building and shadow) to 7%, 6%, 6%, 8%, 9%, and 24% respectively with producer’s accuracy. Besides, 17% and 10% increase obtained in user’s accuracy with MAP (area) feature in classifying road and shadow classes respectively. Moreover, when the object based classification is conducted, it is seen that the OA of pixel based classification is increased further by 1.07%. An increase between 2% and 4% is achieved with producer’s accuracy in soil, vegetation and building classes and an increase between 1% and 3% is achieved by user’s accuracy in soil, road, vegetation and shadow classes. In the end, accurate LULC map is produced with object based RF classification of gabor features added airborne hyperspectral image which is dimensionally reduced with DWT method. Numéro de notice : A2022-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1944453 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1944453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101675
in Geocarto international > vol 37 n° 22 [10/10/2022] . - pp 6643 - 6670[article]Application of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
[article]
Titre : Application of a graph convolutional network with visual and semantic features to classify urban scenes Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Shuai Jin, Auteur ; Zhanlong Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2009-2034 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] matrice de co-occurrence
[Termes IGN] OpenStreetMap
[Termes IGN] Pékin (Chine)
[Termes IGN] point d'intérêt
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] scène urbaineRésumé : (auteur) Urban scenes consist of visual and semantic features and exhibit spatial relationships among land-use types (e.g. industrial areas are far away from the residential zones). This study applied a graph convolutional network with neighborhood information (henceforth, named the neighbour supporting graph convolutional neural network), to learn spatial relationships for urban scene classification. Furthermore, a co-occurrence analysis with visual and semantic features proceeded to improve the accuracy of urban scene classification. We tested the proposed method with the fifth ring road of Beijing with an overall classification accuracy of 0.827 and a Kappa coefficient of 0.769. In comparison with other methods, such as support vector machine, random forest, and general graph convolutional network, the case study showed that the proposed method improved about 10% in urban scene classification. Numéro de notice : A2022-740 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2048834 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2048834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101717
in International journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2009-2034[article]Augmented reality for scene text recognition, visualization and reading to assist visually impaired people / Imene Ouali in Procedia Computer Science, vol 207 (2022)
[article]
Titre : Augmented reality for scene text recognition, visualization and reading to assist visually impaired people Type de document : Article/Communication Auteurs : Imene Ouali, Auteur ; Mohamed Ben Halima, Auteur ; Ali Wali, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] enquête
[Termes IGN] personne malvoyante
[Termes IGN] réalité augmentée
[Termes IGN] reconnaissance de caractères
[Termes IGN] signalisation routière
[Termes IGN] visualisationRésumé : (auteur) Reading traffic signs while driving a car for visually impaired people and people with visual problems is a very difficult task for them. This task is encountered every day, sometimes incorrect reading of traffic signs can lead to very serious results. In particular, the Arabic language is very difficult, making recognizing and viewing Arabic text a difficult task. In this context, we are looking for an effective solution to remove errors and results that can sometimes end someone's life. This article aims to correctly read traffic signs with Arabic text using augmented reality technology. Our system is composed of three modules. The first is text detection and recognition. The second is Text visualization. The third is Text to speech methods conversion. With this system, the user can have two different results. The first result is visual with much-improved text and enhancement. The second result is sound, he can hear the text aloud. This system is very applicable and effective for daily life. To assess the effectiveness of our work, we offer a survey to a group of visually impaired people to give their opinion on the use of our application. The results have been good for most people. Numéro de notice : A2023-010 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1016/j.procs.2022.09.048 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.procs.2022.09.048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102119
in Procedia Computer Science > vol 207 (2022) . - pp 158 - 167[article]Canopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality / Jordi Margalef- Marrase in Forest ecology and management, vol 521 (October-1 2022)
[article]
Titre : Canopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality Type de document : Article/Communication Auteurs : Jordi Margalef- Marrase, Auteur ; Guillem Bagaria, Auteur ; Francisco Lloret, Auteur Année de publication : 2022 Article en page(s) : n° 120427 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie)
[Termes IGN] analyse de données
[Termes IGN] canopée
[Termes IGN] Catalogne (Espagne)
[Termes IGN] changement climatique
[Termes IGN] classification et arbre de régression
[Termes IGN] croissance des arbres
[Termes IGN] dépérissement
[Termes IGN] mortalité
[Termes IGN] Pinus sylvestris
[Termes IGN] Quercus pubescens
[Termes IGN] sécheresse
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) In recent years, Pinus sylvestris die-off and mortality events have occurred across all its range of distribution, usually associated with recurrent droughts induced by climate change. A shift in canopy dominance towards other better adapted co-existing species can be expected, especially in populations located close to their climatic tolerance limits. Herein, we tested, along a local elevational gradient, whether canopy opening resulting from die-off and mortality favours the growth of a non-dominant co-existing tree species (Quercus pubescens) established in the sub-canopy, in comparison to P. sylvestris sub-canopy trees. We also tested whether the growth of both species is associated with local climatic suitability for these species (extracted from SDMs) or, alternatively, with direct measures of micro-climatic variables. Finally, the effect on tree growth of other micro-local factors such as competition, canopy closure and micro-topography was also tested. Sub-canopy tree growth was enhanced overall by canopy opening resulting from P. sylvestris canopy die-off, but this response was stronger in P. sylvestris trees, reinforcing the self-replacement of this species after die-off. This higher growth rate is related to modifications in the micro-local climate (higher temperatures in the wettest quarter). Conversely, Q. pubescens is less sensitive to micro-local climate conditions but it can grow faster than P. sylvestris on stands with no canopy die-off or mortality. In contrast, climatic suitability extracted from SDMs was negatively related to sub-canopy P. sylvestris growth and had no effect on Q. pubescens. These contrasting results support observations at plot scale that P. sylvestris self-replacement is better explained by local environmental conditions than by values of climatic suitability obtained from regional-scale data-sets. Nevertheless, these climatic suitability measures remain consistent with the overall pattern of low seedling recruitment observed in previous works at the rear edge of species' distribution. This study reveals that short-term shifts in species dominance at a local scale will not necessarily occur in the studied P. sylvestris forests following die-off. This finding endorses the notion that micro-local environment and species traits (i.e., light and temperature tolerance, life-history strategies) modulate the capacity for resilience in rear-edge populations that would probably be prone to collapse otherwise. Numéro de notice : A2022-709 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : https://doi.org/10.1016/j.foreco.2022.120427 Date de publication en ligne : 21/07/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120427 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101585
in Forest ecology and management > vol 521 (October-1 2022) . - n° 120427[article]Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images : A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])PermalinkComparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDetecting overmature forests with airborne laser scanning (ALS) / Marc Fuhr in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)PermalinkDeveloping a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkEstimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkIdentify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkInvestigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)PermalinkInvestigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)PermalinkMachine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)PermalinkModelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)Permalink