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Fusing Sentinel-2 and Landsat 8 satellite images using a model-based method / Jakob Sigurdsson in Remote sensing, vol 14 n° 13 (July-1 2022)
[article]
Titre : Fusing Sentinel-2 and Landsat 8 satellite images using a model-based method Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Sveinn E. Armannsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2022 Article en page(s) : n° 3224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] limite de résolution géométrique
[Termes IGN] modèle géométrique de prise de vueRésumé : (auteur) The Copernicus Sentinel-2 (S2) constellation comprises of two satellites in a sun-synchronous orbit. The S2 sensors have three spatial resolutions: 10, 20, and 60 m. The Landsat 8 (L8) satellite has sensors that provide seasonal coverage at spatial resolutions of 15, 30, and 60 m. Many remote sensing applications require the spatial resolutions of all data to be at the highest resolution possible, i.e., 10 m for S2. To address this demand, researchers have proposed various methods that exploit the spectral and spatial correlations within multispectral data to sharpen the S2 bands to 10 m. In this study, we combined S2 and L8 data. An S2 sharpening method called Sentinel-2 Sharpening (S2Sharp) was modified to include the 30 m and 15 m spectral bands from L8 and to sharpen all bands (S2 and L8) to the highest resolution of the data, which was 10 m. The method was evaluated using both real and simulated data. Numéro de notice : A2022-573 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.3390/rs14133224 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.3390/rs14133224 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101289
in Remote sensing > vol 14 n° 13 (July-1 2022) . - n° 3224[article]A second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)
[article]
Titre : A second-order attention network for glacial lake segmentation from remotely sensed imagery Type de document : Article/Communication Auteurs : Shidong Wang, Auteur ; Maria V. Peppa, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 289 - 301 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] changement climatique
[Termes IGN] covariance
[Termes IGN] image Landsat-8
[Termes IGN] Inde
[Termes IGN] itération
[Termes IGN] lac glaciaire
[Termes IGN] réflectance de surface
[Termes IGN] segmentation d'image
[Termes IGN] tenseurRésumé : (auteur) Climate change is increasing the risk of glacial lake outburst floods (GLOFs) in many of the world’s most vulnerable and high mountain regions. Simultaneously, remote sensing technologies now facilitate continuous monitoring of glacial lake evolution around the globe, although accurate and reliable automated glacial lake mapping from satellite data remains challenging. In this study, a Second-order Attention Network (SoAN) is devised for the automated segmentation of lakes from satellite imagery. In particular, a novel Second-order Attention Module (SoAM) is proposed to capture the long-range spatial dependencies and establish channel attention derived from the covariance representations of local features. Furthermore, as the dimensions of the input and output tensors are identical and it simply relies on matrix calculations, the proposed SoAM can be embedded into different positions of a given architecture while maintaining similar reference speed. The designed network is implemented on Landsat-8 imagery and outputs are compared against representative deep learning models, demonstrating improved results with a Dice of 81.02% and a F2 Score of 85.17%. Numéro de notice : A2022-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.007 Date de publication en ligne : 29/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100814
in ISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 289 - 301[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2022071 SL Revue Centre de documentation Revues en salle Disponible Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique / Syaza Rozali in Geocarto international, vol 37 n° 11 ([15/06/2022])
[article]
Titre : Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique Type de document : Article/Communication Auteurs : Syaza Rozali, Auteur ; Zulkiflee Abd Latif, Auteur ; Nor Aizam Adnan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3247 - 3264 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] image Landsat-OLI
[Termes IGN] MalaisieRésumé : (auteur) The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar’s test p-value ( Numéro de notice : A2022-586 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1852610 Date de publication en ligne : 27/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1852610 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101360
in Geocarto international > vol 37 n° 11 [15/06/2022] . - pp 3247 - 3264[article]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]The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)
[article]
Titre : The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria Type de document : Article/Communication Auteurs : Alfred S. Alademomi, Auteur ; Chukwuma J. Okolie, Auteur ; Olagoke E. Daramola, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 299 - 314 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] corrélation temporelle
[Termes IGN] détection de changement
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] Lagos
[Termes IGN] Normalized Difference Built-up Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] température au solRésumé : (auteur) In recent times, there has been renewed interest in understanding the dynamics of land cover change and its relationship with several environmental parameters. This study assesses the interrelationship between land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and land cover change in Amuwo-Odofin Local Government Area of Lagos State, Nigeria. Multi-temporal and multi-spectral Landsat imageries for years 2002, 2013, 2016, and 2019 served as the primary dataset. Using the parallelepiped classifier, the imageries were classified into five land cover classes — mixed vegetation, bare land, built-up area, water body, and wetland. The spectral indices (NDVI and NDBI) were computed and the LST was determined using a single-channel algorithm. Land cover transition matrices were calculated to examine the proportion of land cover change between classes, including the unchanged areas. Pearson’s correlation analysis enabled an analysis of the interdependence or interrelationship in the distribution of the parameters. From 2002 to 2019, the highest land cover transitions recorded were bare land to built-up area (12.64 km2), mixed vegetation to built-up area (21.55 km2), wetland to mixed vegetation (8.87 km2), and mixed vegetation to bare land (8.46 km2). There was a negative correlation between LST and NDVI, and between NDVI and NDBI. The distribution of the LST, NDVI, and NDBI varied correspondingly in accordance with land cover changes. The increase in built-up area could be the major driver of the observed changes in LST, NDBI, and NDVI, with an observed relationship that NDBI and LST values increase with increase in built-up areas. Numéro de notice : A2022-463 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-022-00434-2 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1007/s12518-022-00434-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100790
in Applied geomatics > vol 14 n° 2 (June 2022) . - pp 299 - 314[article]Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa / Shenelle Lottering in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkSimulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data / Zihao Huang in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkThe integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)PermalinkUrban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)PermalinkDynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkEstimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image / Sinan Kaptan in Geocarto international, vol 37 n° 5 ([01/03/2022])PermalinkExploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China / Zhen Li in Sustainable Cities and Society, vol 78 (March 2022)PermalinkFeasibility 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)PermalinkA novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)Permalink