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A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images / Hessah Albanwan in Photogrammetric record, vol 37 n° 180 (December 2022)
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Titre : A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images Type de document : Article/Communication Auteurs : Hessah Albanwan, Auteur ; Rongjun Qin, Auteur Année de publication : 2022 Article en page(s) : pp 385 - 409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couple stéréoscopique
[Termes IGN] modèle stéréoscopique
[Termes IGN] précision géométrique (imagerie)Résumé : (auteur) Deep-learning (DL) stereomatching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo-images lacking a systematic evaluation on how robust DL methods are on satellite stereo-images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereomatching methods through hundreds of multi-date multi-site satellite stereopairs with varying geometric configurations, against the traditional well-practiced Census-semi-global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalise well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereopairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground-view). Numéro de notice : A2022-938 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12430 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.1111/phor.12430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102684
in Photogrammetric record > vol 37 n° 180 (December 2022) . - pp 385 - 409[article]Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory / Charlotte Labit in Biodiversity & Conservation, vol 31 n° 13-14 (December 2022)
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Titre : Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory Type de document : Article/Communication Auteurs : Charlotte Labit, Auteur ; Ingrid Bonhême , Auteur ; Sébastien Delhaye
, Auteur
Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 3257 - 3283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Alpes-de-haute-provence (04)
[Termes IGN] Alpes-maritimes (06)
[Termes IGN] analyse comparative
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Drôme (26)
[Termes IGN] habitat (nature)
[Termes IGN] habitat forestier
[Termes IGN] incertitude des données
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] surveillance écologique
[Vedettes matières IGN] Inventaire forestierMots-clés libres : algorithm inspired by the habitat identification key used in the field Résumé : (auteur) The monitoring of habitats at plant association level, has been developed by the French-National Forest Inventory (NFI) progressively since 2011, whereas ecological and floristic data exist since the mid-1980s. The NFI habitat monitoring is the French tool of surveillance of forest habitats decreed by Natura 2000 Directive (article 11). Determination of plant association in NFI plots concerns all the habitats, whether they are of community interest or not. The objective of this study is to compare different methods of automatic classification of floristic and ecological surveys into forest habitat groups. Indeed, enriching the old surveys, which contain only ecological, floristic and trees data, with information on habitats would increase the accuracy of the calculated statistical results on habitats. The uncertainty of the attribution of a habitat outside the field (ex-situ) by experts was quantified by comparison with the determination in the field (in situ). This result was used as a benchmark to compare to the error rates obtained by two methods of automatic classification: an algorithm inspired by the habitat identification key used in the field and Random forest, a learning classification method. The classification performance was evaluated for three levels of habitat groupings. The results showed that the lower the level of clustering, the higher the error rate. Depending on the classification method used and the level of aggregation, the error rates varied between 5 and 15%. In all cases, the error rates were below the estimated uncertainty of the expert attribution of ex-situ habitat. Numéro de notice : A2022-696 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10531-022-02487-6 Date de publication en ligne : 25/10/2022 En ligne : https://doi.org/10.1007/s10531-022-02487-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101980
in Biodiversity & Conservation > vol 31 n° 13-14 (December 2022) . - pp 3257 - 3283[article]Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery / Thomas A. Lake in Remote sensing in ecology and conservation, vol 8 n° 6 (December 2022)
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Titre : Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery Type de document : Article/Communication Auteurs : Thomas A. Lake, Auteur ; Ryan D. Briscoe Runquist, Auteur ; David A. Moeller, Auteur Année de publication : 2022 Article en page(s) : pp 875 - 889 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] espèce exotique envahissante
[Termes IGN] image Worldview
[Termes IGN] PlanetScope
[Termes IGN] série temporelleRésumé : (auteur) Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge (Euphorbia virgata), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview-2 and Planetscope satellites. Worldview-2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short-term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview-2 model. Across models, most false-positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid-season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red-edge and near-infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large-scale, remote and data-sparse areas. Numéro de notice : A2023-033 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.288 En ligne : https://doi.org/10.1002/rse2.288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102295
in Remote sensing in ecology and conservation > vol 8 n° 6 (December 2022) . - pp 875 - 889[article]Discriminating pure Tamarix species and their putative hybrids using field spectrometer / Solomon G. Tesfamichael in Geocarto international, vol 37 n° 25 ([01/12/2022])
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Titre : Discriminating pure Tamarix species and their putative hybrids using field spectrometer Type de document : Article/Communication Auteurs : Solomon G. Tesfamichael, Auteur ; Solomon W. Newete, Auteur ; Elhadi Adam, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7733 - 7752 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] apprentissage automatique
[Termes IGN] canopée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce exotique envahissante
[Termes IGN] essence indigène
[Termes IGN] Extreme Gradient Machine
[Termes IGN] feuille (végétation)
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image Worldview
[Termes IGN] spectroradiomètre
[Termes IGN] Tamarix (genre)Résumé : (auteur) South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, non-native and hybrid Tamarix species recorded in South Africa. Leaf- and canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species. Numéro de notice : A2022-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1983033 Date de publication en ligne : 27/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1983033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102661
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 7733 - 7752[article]Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)
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Titre : Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale Type de document : Article/Communication Auteurs : Shengwu Qin, Auteur ; Shuangshuang Qiao, Auteur ; Jingyu Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2709 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] éboulement
[Termes IGN] hétérogénéité spatiale
[Termes IGN] prévention des risquesRésumé : (auteur) Susceptibility mapping is an effective means of preventing debris flow disasters. However, previous studies have failed to solve spatial heterogeneity well, especially at the regional scale. The main objective of this study is to solve the spatial heterogeneity of regional-scale debris flow susceptibility (DFS) mapping by establishing a geographic information system (GIS)-based processing framework. The framework was realized by integrating the determination factor (DFactor) model with machine learning models. The DFactor model established different combinations of evaluation factors in each local region and clarified the differing contributions of influencing factors to DFS. To test the feasibility of the framework, the support vector machine (SVM) and two-dimensional convolutional neural network (CNN) were integrated with the DFactor model (DFactor-SVM and DFactor-CNN) to evaluate DFS in Jilin Province, China. The individual models (SVM and CNN) were also used to map the DFS for comparison with the integrated models. For debris flow modeling, 868 debris flow samples were collected and randomly divided into two datasets: 70% of the samples were used for training and the result was used for verification. The results of the receiver operating characteristic curve showed that the integrated models performed better. The DFactor-CNN model had the highest predictive accuracy, followed by the DFactor-SVM, CNN and SVM models. In general, the GIS-based processing framework maximizes the contribution of the influencing factors to debris flows and enhances the prediction ability of models. Furthermore, it provides a reliable means to predict debris flows at the regional scale. Numéro de notice : A2022-854 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05487-5 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1007/s11069-022-05487-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102101
in Natural Hazards > vol 114 n° 3 (December 2022) . - pp 2709 - 2738[article]Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)
PermalinkGeographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)
PermalinkHybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])
PermalinkInstance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
PermalinkIntegration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)
PermalinkA new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions / Rasit Ulug in Journal of geodesy, vol 96 n° 12 (December 2022)
PermalinkSea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)
PermalinkSemantic integration of OpenStreetMap and CityGML with formal concept analysis / Somayeh Ahmadian in Transactions in GIS, vol 26 n° 8 (December 2022)
PermalinkThe simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)
PermalinkUrban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])
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