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A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images / Jing Zeng in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)
[article]
Titre : A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images Type de document : Article/Communication Auteurs : Jing Zeng, Auteur ; Yonghua Sun, Auteur ; Peirun Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par arbre de décision
[Termes IGN] classification semi-dirigée
[Termes IGN] image Landsat-8
[Termes IGN] indice de végétation
[Termes IGN] Kiangsou (Chine)
[Termes IGN] marais salant
[Termes IGN] phénologie
[Termes IGN] réflectance de surfaceRésumé : (auteur) Coastal salt marshes, as a globally significant intertidal ecosystem, are highly productive but extremely fragile and unstable. Mapping coastal salt marshes accurately is the basis of assessing global climate change, biological invasion, and coastal erosion. Using Landsat 8 images, this paper integrated the advantages of pixel- and phenology-based algorithms and vegetation indices in vegetation classification. An enhanced phenology-based vegetation index classification (PVC) algorithm is proposed to obtain the spatial distribution and community composition of coastal salt marshes in Bohai Sea of China accurately and quickly. The results showed that (1) the coastal redness vegetation index (CRVI) can be used to extract Suaeda spp. effectively, and the phenology-based vegetation indices (PVIs) dataset can alleviate the spatial variability of phenology in coastal salt marshes; (2) the crucial phenological periods for identifying coastal salt marshes are May, October, and November, and the optimal PVIs are consistent with the phenological characteristics of salt marshes; (3) during the year 2018–2019, the overall accuracy (OA) of the PVC algorithm in Yancheng coast of Jiangsu Province and Bohai Sea coast reached 80.49 % and 90.8 % respectively. A total of 14,763.39 ha of salt marshes were found in the coastal area of Bohai Sea, and Shandong Province had the most abundant types of salt marshes and the largest area; (4) the classification model based on the PVC algorithm is stable and scalable in 2016–2017 and 2020–2021, with the OA of 89.19% and 86.67% respectively. These results demonstrate the value of the PVC algorithm in vegetation classification, and this study can provide a referable semi-automatic vegetation classification method for other coastal areas. Numéro de notice : A2022-551 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102776 Date de publication en ligne : 10/05/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101154
in International journal of applied Earth observation and geoinformation > vol 110 (June 2022) . - n° 102776[article]Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)
[article]
Titre : Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP Type de document : Article/Communication Auteurs : Haibin Wu, Auteur ; Huaming Zhou, Auteur ; Aili Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cultures
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] Perceptron multicoucheRésumé : (auteur) The precise classification of crop types using hyperspectral remote sensing imaging is an essential application in the field of agriculture, and is of significance for crop yield estimation and growth monitoring. Among the deep learning methods, Convolutional Neural Networks (CNNs) are the premier model for hyperspectral image (HSI) classification for their outstanding locally contextual modeling capability, which facilitates spatial and spectral feature extraction. Nevertheless, the existing CNNs have a fixed shape and are limited to observing restricted receptive fields, constituting a simulation difficulty for modeling long-range dependencies. To tackle this challenge, this paper proposed two novel classification frameworks which are both built from multilayer perceptrons (MLPs). Firstly, we put forward a dilation-based MLP (DMLP) model, in which the dilated convolutional layer replaced the ordinary convolution of MLP, enlarging the receptive field without losing resolution and keeping the relative spatial position of pixels unchanged. Secondly, the paper proposes multi-branch residual blocks and DMLP concerning performance feature fusion after principal component analysis (PCA), called DMLPFFN, which makes full use of the multi-level feature information of the HSI. The proposed approaches are carried out on two widely used hyperspectral datasets: Salinas and KSC; and two practical crop hyperspectral datasets: WHU-Hi-LongKou and WHU-Hi-HanChuan. Experimental results show that the proposed methods outshine several state-of-the-art methods, outperforming CNN by 6.81%, 12.45%, 4.38% and 8.84%, and outperforming ResNet by 4.48%, 7.74%, 3.53% and 6.39% on the Salinas, KSC, WHU-Hi-LongKou and WHU-Hi-HanChuan datasets, respectively. As a result of this study, it was confirmed that the proposed methods offer remarkable performances for hyperspectral precise crop classification. Numéro de notice : A2022-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14112713 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/rs14112713 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101102
in Remote sensing > vol 14 n° 11 (June-1 2022) . - n° 2713[article]The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)
[article]
Titre : The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning Type de document : Article/Communication Auteurs : Elie Morin, Auteur ; Pierre-Alexis Herrault, Auteur ; Yvonnick Guinard, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse du paysage
[Termes IGN] BD Topo
[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] connexité (topologie)
[Termes IGN] corridor biologique
[Termes IGN] extraction de la végétation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] indicateur environnemental
[Termes IGN] milieu urbain
[Termes IGN] Niort
[Termes IGN] planification urbaine
[Termes IGN] Poitiers
[Termes IGN] segmentation d'image
[Termes IGN] Vienne (86)Résumé : (auteur) Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object-based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning. Numéro de notice : A2022-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.ecolind.2022.108930 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.1016/j.ecolind.2022.108930 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100592
in Ecological indicators > vol 139 (June 2022) . - n° 108930[article]Towards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Towards the automated large-scale reconstruction of past road networks from historical maps Type de document : Article/Communication Auteurs : Johannes H. Uhl, Auteur ; Stefan Leyk, Auteur ; Yao-Yi Chiang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de groupement
[Termes IGN] analyse de sensibilité
[Termes IGN] carte ancienne
[Termes IGN] carte routière
[Termes IGN] carte topographique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] données multitemporelles
[Termes IGN] Etats-Unis
[Termes IGN] extraction du réseau routier
[Termes IGN] histoire
[Termes IGN] paysage
[Termes IGN] réseau routier
[Termes IGN] transport routier
[Termes IGN] urbanisationRésumé : (auteur) Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography. Numéro de notice : A2022-947 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101794 Date de publication en ligne : 18/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100182
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101794[article]Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)
[article]
Titre : Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon Type de document : Article/Communication Auteurs : Hermann Tagne, Auteur ; Arnaud Le Bris , Auteur ; David Monkam, Auteur ; Clément Mallet , Auteur Année de publication : 2022 Projets : TOSCA Parcelle / Le Bris, Arnaud Article en page(s) : pp 673 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Cameroun
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] série temporelleRésumé : (auteur) Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolutions. These images are in particular of utter interest for Land-Cover (LC) mapping at large scales. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the Very High Resolution national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic vegetation classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained (>94% in Overall Accuracy), allowing to provide a first step towards finer-grained map retrieval. Numéro de notice : A2022-426 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-3-2022-673-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-673-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100731
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 673 - 680[article]Deep learning for the detection of early signs for forest damage based on satellite imagery / Dennis Wittich in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkK-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkLearning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds / Michael Kölle in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkRailway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkA voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results / N. Homainejad in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkComparative analysis of gradient boosting algorithms for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkResearch on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)Permalink3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)PermalinkChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network / Qinjun Qiu in Transactions in GIS, vol 26 n° 3 (May 2022)PermalinkA context feature enhancement network for building extraction from high-resolution remote sensing imagery / Jinzhi Chen in Remote sensing, vol 14 n° 9 (May-1 2022)Permalink