Centre dedocumentation
scientifique

Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)  

Public [article]  inForests > vol 12 n° 12 (December 2021) . - n° 1768 Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] bande infrarouge [Termes IGN] coefficient de corrélation [Termes IGN] couvert forestier [Termes IGN] détection de contours [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle statistique [Termes IGN] Mongolie intérieure (Chine) [Termes IGN] régression [Termes IGN] surveillance de la végétation Résumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318 [article]

Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)  

Public [article]  inGeodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 559 - 576 Titre : Building detection with convolutional networks trained with transfer learning Type de document : Article/Communication Auteurs : Simon Šanca, Auteur ; Krištof Oštir, Auteur ; Alen Mangafić, Auteur Année de publication : 2021 Article en page(s) : pp 559 - 576 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] données cadastrales [Termes IGN] image aérienne [Termes IGN] image infrarouge couleur [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] orthoimage couleur [Termes IGN] segmentation d'image [Termes IGN] Slovénie Résumé : (Auteur) Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning. Numéro de notice : A2021-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.559-593 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.559-593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99409 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible

MSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 12 (December 2021) . - pp 901 - 906 Titre : MSegnet, a practical network for building detection from high spatial resolution images Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Fang Chen, Auteur ; Ying Dong, Auteur Année de publication : 2021 Article en page(s) : pp 901 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] matrice [Termes IGN] segmentation multi-échelle [Termes IGN] segmentation sémantique Résumé : (Auteur) Building detection in big earth data by remote sensing is crucial for urban development. However, improving its accuracy remains challenging due to complicated background objects and different viewing angles from various remotely sensed images. The hereto proposed methods predominantly focus on multi-scale feature learning, which omits features in multiple aspect ratios. Moreover, postprocessing is required to refine the segmentation performance. We propose modified semantic segmentation (MSegnet), a single-shot semantic segmentation model based on a matrix of convolution layers to extract features in multiple scales and aspect ratios. MSegnet consists of two modules: backbone feature learning and matrix convolution to conduct vertical and horizontal learning. The matrix convolution comprises a set of convolution operations with different aspect ratios. MSegnet is applied to a public building data set that is widely used for evaluation and shown to achieve satisfactory accuracy, compared with the published single-shot methods. Numéro de notice : A2021-898 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00016R2 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.14358/PERS.21-00016R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99296 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021121	SL	Revue	Centre de documentation	Revues en salle	Disponible

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)  

Public [article]  inApplied geomatics > vol 13 n° 4 (December 2021) . - pp 499 - 513 Titre : The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space Type de document : Article/Communication Auteurs : Renato César Dos santos, Auteur ; Mauricio Galo, Auteur ; André C. Carrilho, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 499 - 513 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme de Otsu [Termes IGN] analyse de groupement [Termes IGN] Brésil [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données multitemporelles [Termes IGN] espace urbain [Termes IGN] modèle numérique de surface [Termes IGN] planéité [Termes IGN] semis de points [Termes IGN] seuillage Résumé : (auteur) Building change detection techniques are essential for several urban applications. In this context, multi-temporal airborne LiDAR data has been considered an effective alternative since it has some advantages over conventional photogrammetry. Despite several works in the literature, the automatic class definition with great accuracy and performance remains a challenge in change detection. The developed strategies usually explore training samples or empirical thresholds to discriminate the classes. To overcome this limitation, we proposed an automatic building change detection method based on Otsu algorithm and median planarity attribute computed from eigenvalues. The main contribution corresponds to the automatic and unsupervised identification of building changes. The experiments were conducted using airborne LiDAR data from two epochs: 2012 and 2014. From qualitative and quantitative analysis, the robustness of the proposed method in detecting building changes in urban areas was evaluated, presenting completeness and correctness around 99% and 76%, respectively. Numéro de notice : A2021-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-021-00371-6 Date de publication en ligne : 24/04/2021 En ligne : https://doi.org/10.1007/s12518-021-00371-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99062 [article]

Extracting 3D indoor maps with any shape accurately using building information modeling data / Qi Qiu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 700 Titre : Extracting 3D indoor maps with any shape accurately using building information modeling data Type de document : Article/Communication Auteurs : Qi Qiu, Auteur ; Mingjun Wang, Auteur ; Qingsheng Xie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carroyage [Termes IGN] carte d'intérieur [Termes IGN] carte en 3D [Termes IGN] conception assistée par ordinateur [Termes IGN] détection de contours [Termes IGN] grille [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] service fondé sur la position Résumé : (auteur) Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems. Numéro de notice : A2021-778 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100700 Date de publication en ligne : 14/10/2021 En ligne : https://doi.org/10.3390/ijgi10100700 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98842 [article]

A novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)  

Permalink

A deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)  

Permalink

Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Permalink

Disaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Permalink

Automatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

Permalink

Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)

Permalink

Three-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)  

Permalink

Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Permalink

Multi-scale coal fire detection based on an improved active contour model from Landsat-8 satellite and UAV images / Yanyan Gao in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)  

Permalink

Research on 3D model reconstruction based on a sequence of cross-sectional images / Zhiguo Dong in Machine Vision and Applications, vol 32 n°4 (July 2021)  

Permalink