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Semi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)
[article]
Titre : Semi-supervised label propagation for multi-source remote sensing image change detection Type de document : Article/Communication Auteurs : Fan Hao, Auteur ; Zong-Fang Ma, Auteur ; Hong Peng Tian, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 105249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification barycentrique
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] étiquette
[Termes IGN] filtrage du bruit
[Termes IGN] image multi sourcesRésumé : (auteur) Remote sensing image change detection remains a challenging task. Most existing approaches are based on fully supervised learning, but labeled data are so scarce for change detection. It is difficult to exhibit high detection performance with a limited amount of labeled data. In this paper, we propose a semi-supervised Label Propagation (SSLP) approach for multi-source remote sensing image change detection. First, a clustering label propagation (CLP) method is designed to cluster pre and post images, respectively, and assign pseudo labels to unlabeled pixel pairs that have similar mapping relationships to labeled pixel pairs. Second, a pixel density metric is investigated to filter out the data with low density and retain the data with high density, which can ensure the reliability of the propagated data. Third, a secondary expansion method based on pixel neighborhood is used to generate enough training data for training a classifier. Finally, the effectiveness of SSLP is validated on three real datasets by comparing to other related methods. Numéro de notice : A2023-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105249 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105249 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102292
in Computers & geosciences > vol 170 (January 2023) . - n° 105249[article]The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
[article]
Titre : The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Zhenfeng Shao, Auteur ; Akib Javed, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] automate cellulaire
[Termes IGN] carte thématique
[Termes IGN] classification semi-dirigée
[Termes IGN] détection de changement
[Termes IGN] données vectorielles
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] MNS SRTM
[Termes IGN] modèle dynamique
[Termes IGN] occupation du sol
[Termes IGN] Pakistan
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] utilisation du solRésumé : (auteur) Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation. Numéro de notice : A2023-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.14358/PERS.22-00102R2 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.14358/PERS.22-00102R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102357
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 1 (January 2023) . - pp 47 - 55[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2023011 SL Revue Centre de documentation Revues en salle Disponible Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)
[article]
Titre : Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data Type de document : Article/Communication Auteurs : Ying Quan, Auteur ; Mingze Li, Auteur ; Yuanshuo Hao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2171706 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] espèce végétale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt secondaire
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m2) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas. Numéro de notice : A2023-194 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/15481603.2023.2171706 Date de publication en ligne : 03/02/2023 En ligne : https://doi.org/10.1080/15481603.2023.2171706 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103075
in GIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2171706[article]Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows / Pramaditya Wicaksono in Geocarto international, vol 37 n° 27 ([20/12/2022])
[article]
Titre : Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows Type de document : Article/Communication Auteurs : Pramaditya Wicaksono, Auteur ; Amanda Maishella, Auteur ; Wahyu Lazuardi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 15161 - 15186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte thématique
[Termes IGN] classification par arbre de décision
[Termes IGN] classification pixellaire
[Termes IGN] correction d'image
[Termes IGN] filtrage du bruit
[Termes IGN] herbier marin
[Termes IGN] image PlanetScope
[Termes IGN] IndonésieRésumé : (auteur) Seagrass percent cover is a crucial and influential component of the biophysical characteristics of seagrass beds and is a key parameter for monitoring seagrass conditions. Therefore, the availability of seagrass percent cover maps greatly assists in sustainable coastal ecosystem management. This research aimed to assess the consistency of PlanetScope imagery for seagrass percent cover mapping using two study areas, namely Parang Island and Labuan Bajo, Indonesia. Assessing the consistency of the PlanetScope imagery performance in seagrass percent cover mapping helps understand the effects of variations in the image quality on its performance in monitoring changes in seagrass cover. Percent cover maps were derived using object-based image analysis (image segmentation and random forest) and pixel-based random forest algorithm. Accuracy assessment and consistency analysis were conducted on the basis of the following three approaches: overall accuracy consistency, agreement percentage and consistent pixel locations. Results show that PlanetScope images can fairly consistently map seagrass percent cover for a specific area across different dates. However, these images produced different levels of accuracy when used for mapping in seagrass meadows with various characteristics and benthic cover complexities. The mapping accuracy (OA–overall accuracy) and consistency (AP–agreement percentage) in patchy seagrass meadows (Parang Island, mean OA 18.4%–38.6%, AP 44.1%–70.3%) are different from those in continuous seagrass meadows (Labuan Bajo, OA 43.0%–56.2%, and AP 41.8%–55.8%). Moreover, PlanetScope images are consistent when used for mapping seagrasses with low and high percent covers but strive to obtain good consistency for medium percent cover due to the combination of seagrass and non-seagrass in a pixel. Furthermore, images with relatively similar image acquisition conditions (i.e., winds, aerosol optical depth, signal-to-noise ratio, and sunglint intensity) produce better consistency. The OA is related to the image acquisition conditions, whilst the AP is related to variation in these conditions. Nevertheless, PlanetScope is still the best high spatial resolution image that provides daily acquisition and is highly beneficial for various applications in tropical areas with persistent cloud coverage. Numéro de notice : A2022-932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2096122 Date de publication en ligne : 06/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2096122 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102668
in Geocarto international > vol 37 n° 27 [20/12/2022] . - pp 15161 - 15186[article]Automatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)
[article]
Titre : Automatic registration method of multi-source point clouds based on building facades matching in urban scenes Type de document : Article/Communication Auteurs : Yumin Tan, Auteur ; Yanzhe Shi, Auteur ; Yunxin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 767 - 782 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] appariement de formes
[Termes IGN] appariement de points
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] fusion de données multisource
[Termes IGN] modélisation 3D
[Termes IGN] photogrammétrie aérienne
[Termes IGN] points registration
[Termes IGN] Ransac (algorithme)
[Termes IGN] recalage de données localisées
[Termes IGN] scène urbaine
[Termes IGN] superposition de donnéesRésumé : (auteur) Both UAV photogrammetry and lidar have become common in deriv- ing three-dimensional models of urban scenes, and each has its own advantages and disadvantages. However, the fusion of these multisource data is still challenging, in which registration is one of the most important stages. In this paper, we propose a method of coarse point cloud registration which consists of two steps. The first step is to extract urban building facades in both an oblique photogrammetric point cloud and a lidar point cloud. The second step is to align the two point clouds using the extracted building facades. Object Vicinity Distribution Feature (Dijkman and Van Den Heuvel 2002) is introduced to describe the distribution of building facades and register the two heterologous point clouds. This method provides a good initial state for later refined registration process and is translation, rotation, and scale invariant. Experiment results show that the accuracy of this proposed automatic registration method is equiva- lent to the accuracy of manual registration with control points. Numéro de notice : A2022-882 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00069R3 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.14358/PERS.22-00069R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102206
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