Centre dedocumentation
scientifique

Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 4410416 Titre : Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection Type de document : Article/Communication Auteurs : David Alejandro Jimenez-Sierra, Auteur ; David Alfredo Quintero-Olaya, Auteur ; Juan Carlos Alvear-Muñoz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4410416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] détection de changement [Termes IGN] graphe [Termes IGN] image multibande [Termes IGN] image radar moirée [Termes IGN] Kappa de Cohen [Termes IGN] lissage de données [Termes IGN] processus gaussien [Termes IGN] réseau sémantique [Termes IGN] segmentation d'image [Termes IGN] seuillage [Termes IGN] superpixel Résumé : (auteur) Graph-based methods are promising approaches for traditional and modern techniques in change detection (CD) applications. Nonetheless, some graph-based approaches omit the existence of useful priors that account for the structure of a scene, and the inter- and intra-relationships between the pixels are analyzed. To address this issue, in this article, we propose a framework for CD based on graph fusion and driven by graph signal smoothness representation. In addition to modifying the graph learning stage, in the proposed model, we apply a Gaussian mixture model for superpixel segmentation (GMMSP) as a downsampling module to reduce the computational cost required to learn the graph of the entire images. We carry out tests on 14 real cases of natural disasters, farming, and construction. The dataset contains homogeneous cases with multispectral (MS) and synthetic aperture radar (SAR) images, along with heterogeneous cases that include MS/SAR images. We compare our approach against probabilistic thresholding, unsupervised learning, deep learning, and graph-based methods. In terms of Cohen’s kappa coefficient, our proposed model based on graph signal smoothness representation outperformed state-of-the-art approaches in ten out of 14 datasets. Numéro de notice : A2022-379 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3168126 Date de publication en ligne : 18/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3168126 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100643 [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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102700 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 spectrale Résumé : (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 [article]

Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 615 - 630 Titre : Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds Type de document : Article/Communication Auteurs : Longjie Ye, Auteur ; Ka Zhang, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 615 - 630 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme de filtrage [Termes IGN] classification barycentrique [Termes IGN] courbure [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fonction spline d'interpolation [Termes IGN] Kappa de Cohen [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de terrain [Termes IGN] processus gaussien [Termes IGN] semis de points Résumé : (Auteur) This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges. Numéro de notice : A2021-671 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.20-00080 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.87.20-00080 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98820 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021091	SL	Revue	Centre de documentation	Revues en salle	Disponible

An adaptive filtering algorithm of multilevel resolution point cloud / Youyuan Li in Survey review, Vol 53 n° 379 (July 2021)  

Public [article]  inSurvey review > Vol 53 n° 379 (July 2021) . - pp 300 - 311 Titre : An adaptive filtering algorithm of multilevel resolution point cloud Type de document : Article/Communication Auteurs : Youyuan Li, Auteur ; Jian Wang, Auteur ; Bin Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 300 - 311 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de filtrage [Termes IGN] analyse multirésolution [Termes IGN] classification ascendante hiérarchique [Termes IGN] données lidar [Termes IGN] filtrage de points [Termes IGN] filtre adaptatif [Termes IGN] interpolation spatiale [Termes IGN] Kappa de Cohen [Termes IGN] octree [Termes IGN] pente [Termes IGN] semis de points [Termes IGN] seuillage de points Résumé : (auteur) The existing filtering methods for airborne LiDAR point cloud have low accuracy. An adaptive filtering algorithm is proposed which is improved based on multilevel resolution algorithm. First double index structure of Octree and KDtree is established. Then the initial reference surface is constructed by ground seed points. According to the slope fluctuation situation, the grid resolution of the ground referential surface is adjusted in an adaptive way. Finally, the refined surface is formed gradually by multilevel renewing resolution to provide filtered point cloud with high accuracy. Experimental results show that the error of Type II can be effectively reduced, the average Kappa coefficient increases by 0.53% and the average total error decreases by 0.44% compared with multiresolution hierarchical classification algorithm. The result tested by practically measured data shows that Kappa coefficient can reach 90%. Especially, it maintains advantages of high accuracy under complex topographic environment. Numéro de notice : A2021-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1755163 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1080/00396265.2020.1755163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98042 [article]

A high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 421 - 430 Titre : A high-resolution satellite DEM filtering method assisted with building segmentation Type de document : Article/Communication Auteurs : Yihui Li, Auteur ; Fang Gao, Auteur ; Wentao Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 421 - 430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] filtrage numérique d'image [Termes IGN] filtre adaptatif [Termes IGN] image à haute résolution [Termes IGN] Kappa de Cohen [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] point d'appui [Termes IGN] segmentation d'image Résumé : (Auteur) Digital elevation model (DEM) filtering is critical in DEM production, and large-area meter-level resolution DEM is mainly generated from high-resolution satellite images. However, the current DEM filtering methods are mostly aimed at laser scanning data and tend to excessively remove ground points when processing a satellite digital surface model (DSM). To accurately filter out buildings and preserve terrain, we propose a DEM filtering algorithm using building segmentation results of orthophoto. Based on morphological filtering, our method estimates the probability of being a built-up area or mountains for DSM, and according to this probability the filtering parameters are adaptively adjusted. For robustness, our method performs the above filtering operation on DSM through a sliding-window approach, and finally the nonground points are determined by the votes of multiple filtering. Experiments against six representative data sets have shown that our method achieved superior performance than classical algorithms and commercial software. Numéro de notice : A2021-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.6.421 Date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.6.421 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97827 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021061	SL	Revue	Centre de documentation	Revues en salle	Disponible

A water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])  

Permalink

A review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Permalink

An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping / Qi Zhou in Transactions in GIS, Vol 23 n° 6 (November 2019)  

Permalink

A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Permalink

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

Permalink

Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October-1 2019)  

Permalink

Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Permalink

Temporal accuracy in urban growth forecasting : a study using the SLEUTH model / Gargi Chaudhuri in Transactions in GIS, vol 18 n° 2 (April 2014)  

Permalink

A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)  

Permalink

Graph-based feature selection for object-oriented classification in VHR airborne imagery / T. Chen in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)  

Permalink