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Digital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy / Sergio Jiménez-Jiménez in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 285 Titre : Digital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy Type de document : Article/Communication Auteurs : Sergio Jiménez-Jiménez, Auteur ; Waldo Ojeda-Bustamante, Auteur ; Mariana de Jesús Marcial, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 285 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] capteur aérien [Termes IGN] estimation de précision [Termes IGN] étalonnage d'instrument [Termes IGN] géomorphologie [Termes IGN] image captée par drone [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation géométrique de prise de vue [Termes IGN] photogrammétrie aérienne [Termes IGN] plan de vol [Termes IGN] point d'appui [Termes IGN] précision des mesures [Termes IGN] qualité des données [Termes IGN] structure-from-motion Résumé : (auteur) Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized. Numéro de notice : A2021-394 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi10050285 date de publication en ligne : 29/04/2021 En ligne : https://doi.org/10.3390/ijgi10050285 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97680 [article]

Refinement of interferometric SAR parameters using digital terrain model as an external reference / Jyunpei Uemoto in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 34 - 43 Titre : Refinement of interferometric SAR parameters using digital terrain model as an external reference Type de document : Article/Communication Auteurs : Jyunpei Uemoto, Auteur Année de publication : 2021 Article en page(s) : pp 34 - 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] ajustement de paramètres [Termes IGN] empreinte [Termes IGN] hauteur (coordonnée) [Termes IGN] hauteur des arbres [Termes IGN] hauteur du bâti [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] jeu de données [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] point d'appui [Termes IGN] radar aéroporté à visée latérale Résumé : (auteur) Numéro de notice : A2021-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.017 date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97309 [article]

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Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 8 (April-2 2021) . - n° 1597 Titre : Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing Type de document : Article/Communication Auteurs : Shangharsha Thapa, Auteur ; Virginia Garcia Millan, Auteur ; Lars Eklundh, Auteur Année de publication : 2021 Article en page(s) : n° 1597 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse multiéchelle [Termes IGN] capteur multibande [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] réflectance spectrale [Termes IGN] série temporelle [Termes IGN] Suède [Termes IGN] surveillance forestière [Termes IGN] variation saisonnière Résumé : (auteur) The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of Numéro de notice : A2021-382 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081597 date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081597 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97633 [article]

DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques / Ali H. Ahmed Suliman in Geocarto international, vol 36 n° 7 ([01/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 7 [01/04/2021] . - pp 803 - 819 Titre : DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques Type de document : Article/Communication Auteurs : Ali H. Ahmed Suliman, Auteur ; W. Gumindoga, Auteur ; Taymoor A. Awchi, Auteur ; Ayob Katimon, Auteur Année de publication : 2021 Article en page(s) : pp 803 - 819 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Advanced Spaceborne Thermal Emission and Reflection Radiometer [Termes IGN] analyse comparative [Termes IGN] bassin hydrographique [Termes IGN] carte topographique [Termes IGN] Iran [Termes IGN] limite de résolution géométrique [Termes IGN] MNS ASTER [Termes IGN] modèle numérique de surface [Termes IGN] plus proche voisin (algorithme) [Termes IGN] ruissellement Résumé : (Auteur) The accurate estimation of terrain characteristics is central in rainfall runoff modelling. In this study, influences of Digital Elevation Models (DEMs) obtained from different sources, resolutions and rescaling techniques are compared for Peak flow prediction in a large-scale watershed by the Topographic driven model (TOPMODEL). The comparison includes graphical representation and statistical assessments using daily time series data. As a result, DEM extracted from contour map (DEM-Con) showed better performance when DEM resolutions increased, but the Advanced Space-borne Thermal Emission and Reflection Radiometer (DEM-Aster) continued to achieve less Relative Error (RE) at low resolution. Moreover, better RE values were found at cubic convolution technique to predict the peaks followed by nearest neighbor and bilinear. In addition, this study indicated that DEM resolution is more sensitive factor for TOPMODEL simulation compared to DEM sources and rescaling techniques for streamflow and peaks prediction. Numéro de notice : A2021-295 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622599 date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1622599 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97355 [article]

Unsupervised pansharpening based on self-attention mechanism / Ying Qu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3192 - 3208 Titre : Unsupervised pansharpening based on self-attention mechanism Type de document : Article/Communication Auteurs : Ying Qu, Auteur ; Razieh Kaviani Baghbaderani, Auteur ; Hairong Qi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3192 - 3208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] image multibande [Termes IGN] pansharpening (fusion d'images) [Termes IGN] pouvoir de résolution géométrique [Termes IGN] précision infrapixellaire [Termes IGN] reconstruction d'image [Termes IGN] segmentation d'image Résumé : (auteur) Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high spatial resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the upsampled MSI. Recent deep learning endeavors are mostly supervised assuming that the HR MSI is available, which is unrealistic especially for satellite images. Nonetheless, these methods could not fully exploit the rich spectral characteristics in the MSI. Due to the wide existence of mixed pixels in satellite images where each pixel tends to cover more than one constituent material, pansharpening at the subpixel level becomes essential. In this article, we propose an unsupervised pansharpening (UP) method in a deep-learning framework to address the abovementioned challenges based on the self-attention mechanism (SAM), referred to as UP-SAM. The contribution of this article is threefold. First, the SAM is proposed where the spatial varying detail extraction and injection functions are estimated according to the attention representations indicating spectral characteristics of the MSI with subpixel accuracy. Second, such attention representations are derived from mixed pixels with the proposed stacked attention network powered with a stick-breaking structure to meet the physical constraints of mixed pixel formulations. Third, the detail extraction and injection functions are spatial varying based on the attention representations, which largely improves the reconstruction accuracy. Extensive experimental results demonstrate that the proposed approach is able to reconstruct sharper MSI of different types, with more details and less spectral distortion compared with the state-of-the-art. Numéro de notice : A2021-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3009207 date de publication en ligne : 23/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3009207 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97394 [article]

Use of ground penetrating radar in the evaluation of wood structures: A review / Brunela Pollastrelli Rodrigues in Forests, vol 12 n° 4 (April 2021)  

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Implementation of close range photogrammetry using modern non-metric digital cameras for architectural documentation / Mariem A. Elhalawani in Geodesy and cartography, vol 47 n° 1 (January 2021)  

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Passive radar imaging of ship targets with GNSS signals of opportunity / Debora Pastina in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

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Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle / Lucas Santos Santana in European journal of remote sensing, vol 54 n° 1 (2021)  

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Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain / R. Niederheiser in GIScience and remote sensing, vol 58 n° 1 (February 2021)  

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Fusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

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Intelligent embedded camera for robust object tracking on mobile platform / Imane Salhi (2021)  

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Mask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors / Emilio Guirado in Sensors, vol 21 n° 1 (January 2021)  

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Polarimetric Synthetic Aperture Radar / Irena Hajnsek (2021)  

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Remote sensing and GIS / Basudeb Bhatta (2021)

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