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A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples / Ali Jamali in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)
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Titre : A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples Type de document : Article/Communication Auteurs : Ali Jamali, Auteur ; Masoud Mahdianpari, Auteur ; fariba Mohammadimanesh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103095 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Canada
[Termes IGN] carte thématique
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] réseau antagoniste génératif
[Termes IGN] zone humideRésumé : (auteur) Wetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of enough reference data for accurate large-scale wetland mapping. As such, the main objective of this study was to investigate the efficient deep-learning models for generating high-resolution and temporally rich training datasets for wetland mapping. The Sentinel-1 and Sentinel-2 satellites from the European Copernicus program deliver radar and optical data at a high temporal and spatial resolution. These Earth observations provide a unique source of information for more precise wetland mapping from space. The second objective was to investigate the efficiency of vision transformers for complex landscape mapping. As such, we proposed a 3D Generative Adversarial Network (3D GAN) to best achieve these two objectives of synthesizing training data and a Vision Transformer model for large-scale wetland classification. The proposed approach was tested in three different study areas of Saint John, Sussex, and Fredericton, New Brunswick, Canada. The results showed the ability of the 3D GAN to stimulate and increase the number of training data and, as a result, increase the accuracy of wetland classification. The quantitative results also demonstrated the capability of jointly using data augmentation, 3D GAN, and Vision Transformer models with overall accuracy, average accuracy, and Kappa index of 75.61%, 73.4%, and 71.87%, respectively, using a disjoint data sampling strategy. Therefore, the proposed deep learning method opens a new window for large-scale remote sensing wetland classification. Numéro de notice : A2022-828 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103095 Date de publication en ligne : 08/11/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102012
in International journal of applied Earth observation and geoinformation > vol 115 (December 2022) . - n° 103095[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]Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine / Xingwen Lin in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)
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Titre : Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine Type de document : Article/Communication Auteurs : Xingwen Lin, Auteur ; Shengbiao Wu, Auteur ; Bin Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] albedo
[Termes IGN] bande spectrale
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] Google Earth Engine
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de transfert radiatif
[Termes IGN] phénologie
[Termes IGN] réflectance de surfaceRésumé : (auteur) Land surface albedo plays an important role in controlling the surface energy budget and regulating the biophysical processes of natural dynamics and anthropogenic activities. Satellite remote sensing is the only practical approach to estimate surface albedo at regional and global scales. It nevertheless remains challenging for current satellites to capture fine-scale albedo variations due to their coarse spatial resolutions from tens to hundreds of meters. The emerging Sentinel-2 satellites, with a high spatial resolution of 10 m and an approximate 5-day revisiting cycle, provide a promising solution to address these observational limitations, yet their potentials remain underexplored. In this study, we integrated the Sentinel-2 observations with an updated direct estimation approach to improve the estimation and monitoring of fine-scale surface albedo. To enable the capability of the direct estimation approach at a 10-m scale, we combined the 10-m resolution European Space Agency (ESA) WorldCover land cover data and the 500-m resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/albedo product to build a high-quality and representative BRDF training database. To evaluate our approach, we proposed an integrated evaluation framework leveraging 3-D physical model simulations, ground measurements, and satellite observations. Specifically, we first simulated a comprehensive dataset of Sentinel-2-like surface reflectance and broadband albedo across a variety of geometric configurations using the MODIS BRDF training samples. With this dataset, we built the Look-Up-Tables (LUTs) that connect surface broadband albedo and Sentinel-2 reflectance through a direct angular bin-based linear regression approach, and further coupled these LUTs with the Google Earth Engine (GEE) cloud-computing platform. We next evaluated the proposed algorithm at two spatial levels: (1) 10-m scale for absolute accuracy assessment using the references from the Discrete Anisotropic Radiative Transfer (DART) simulations and flux-site observations, and (2) 500-m scale for large-scale mapping assessment by comparing the estimated albedo with the MODIS albedo product. Lastly, we presented four examples to show the capability of Sentinel-2 albedo in detecting fine-scale characteristics of vegetation and urban covers. Results show that: (1) the proposed algorithm accurately estimates surface albedo from Sentinel-2-like reflectance across different landscape configurations (overall root-mean-square-error (RMSE) = 0.018, bias = 0.005, and coefficient of determination (R2) = 0.88); (2) the Sentinel-2-derived surface albedo agrees well with ground measurements (overall RMSE = 0.030, bias = -0.004, and R2 = 0.94) and MODIS products (overall RMSE = 0.030, bias = 0.021, and R2 = 0.97); and (3) Sentinel-2-derived albedo accurately captures seasonal leaf phenology and rapid snow events, and detects the interspecific (or interclass) variations of tree species and colored urban rooftops. These results demonstrate the capability of the proposed approach to map high-resolution surface albedo from Sentinel-2 satellites over large spatial and temporal contexts, suggesting the potential of using such fine-scale datasets to improve our understanding of albedo-related biophysical processes in the coupled human-environment system. Numéro de notice : A2022-823 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.09.016 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101999
in ISPRS Journal of photogrammetry and remote sensing > vol 194 (December 2022) . - pp 1 - 20[article]Extracting built-up land area of airports in China using Sentinel-2 imagery through deep learning / Fanxuan Zeng in Geocarto international, vol 37 n° 25 ([01/12/2022])
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Titre : Extracting built-up land area of airports in China using Sentinel-2 imagery through deep learning Type de document : Article/Communication Auteurs : Fanxuan Zeng, Auteur ; Xin Wang, Auteur ; Mengqi Zha, Auteur Année de publication : 2022 Article en page(s) : pp 7753 - 7773 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aéroport
[Termes IGN] apprentissage profond
[Termes IGN] architecture de réseau
[Termes IGN] Chine
[Termes IGN] détection du bâti
[Termes IGN] image Sentinel-MSIRésumé : (auteur) In China, airports have a profound impact on people’s lives, and understanding their dimensions has great significance for research and development. However, few existing airport databases contain such details, which can be reflected indirectly by the built-up land in the airport. In this study, a deep learning-based method was used for extraction of built-up land of airports in China using Sentinel-2 imagery and for further estimating their area. Here, a benchmark generation method is introduced by fusing two reference maps and cropping images into patches. Following this, a series of experiments were conducted to evaluate the network architectures and select the positive impact bands in Sentinel-2 imagery. A well-trained model was used to extract the built-up land for China airports, and the relationship between China airports’ built-up land and the carrying capacity of air transportation was further analysed. Results show that ResUNet-a outperformed U-Net, ResUNet, and SegNet, and the B2, B4, B6, B11, and B12 bands of Sentinel-2 had a positive impact on built-up land extraction. A well-trained model with an overall accuracy of 0.9423 and an F1 score of 0.9041 and 434 China airports’ built-up land was extracted. The four most developed airports are located in Beijing, Shanghai, and Guangzhou, which matches China’s political and economic development. The area of built-up land influenced the passenger throughput and aircraft movements. The total area influenced the cargo throughput, and we found a certain correlation among the built-up land, carrying capacity, and nighttime light. Numéro de notice : A2022-929 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1983034 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1983034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102662
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 7753 - 7773[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)
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Titre : Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands Type de document : Article/Communication Auteurs : Katrin Krzepek, Auteur ; Jacob Schmidt, Auteur ; Dorota Iwaszczuk, Auteur Année de publication : 2022 Article en page(s) : pp 561 - 575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage non-dirigé
[Termes IGN] aquifère
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] bande C
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fusion d'images
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] puits de carbone
[Termes IGN] seuillage d'image
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] tourbièreRésumé : (auteur) Peatlands as natural carbon sinks have a major impact on the climate balance and should therefore be monitored and protected. The hydrology of the peatland serves as an indicator of the carbon storage capacity. Hence, we investigate the question how suitable different remote sensing data are for monitoring the size of open water surface and the water table depth (WTD) of a peatland ecosystem. Furthermore, we examine the potential of combining remote sensing data for this purpose. We use C-band synthetic aperture radar (SAR) data from Sentinel-1 and multi-spectral data from Sentinel-2. The radar backscatter σ0, the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) are calculated and used for consideration of the WTD and the lake size. For the measurement of the lake size, we implement and investigate the methods: random forest, adaptive thresholding and an analysis according to the Dempster–Shafer theory. Correlations between WTD and the remote sensing data σ0 as well as NDWI are investigated. When looking at the individual data sets the results of our case study show that the VH polarized σ0 data produces the clearest delineation of the peatland lake. However the adaptive thresholding of the weighted fusion image of σ0-VH, σ0-VV and MNDWI, and the random forest algorithm with all three data sets as input proves to be the most suitable for determining the lake area. The correlation coefficients between σ0/NDWI and WTD vary greatly and lie in ranges of low to moderate correlation. Numéro de notice : A2022-942 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41064-022-00216-w Date de publication en ligne : 06/09/2022 En ligne : https://doi.org/10.1007/s41064-022-00216-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102876
in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science > vol 90 n° 6 (December 2022) . - pp 561 - 575[article]Harvested area did not increase abruptly-how advancements in satellite-based mapping led to erroneous conclusions / Johannes Breidenbach in Annals of Forest Science, vol 79 n° 1 (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 geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (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)
PermalinkMapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)
PermalinkPotentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. / Guido Ceccherini in Annals of Forest Science, vol 79 n° 1 (2022)
PermalinkReconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (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)
PermalinkSpatio-temporal patterns of wildfires in Siberia during 2001–2020 / Oleg Tomshin in Geocarto international, vol 37 n° 25 ([01/12/2022])
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)
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