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GNSS integer ambiguity posterior probability calculation with controllable accuracy / Zemin Wu in Journal of geodesy, vol 96 n° 8 (August 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 8 (August 2022) . - n° 53 Titre : GNSS integer ambiguity posterior probability calculation with controllable accuracy Type de document : Article/Communication Auteurs : Zemin Wu, Auteur Année de publication : 2022 Article en page(s) : n° 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] ambiguïté entière [Termes IGN] erreur moyenne quadratique [Termes IGN] estimation de précision [Termes IGN] incertitude de position [Termes IGN] positionnement par GNSS [Termes IGN] résolution d'ambiguïté Résumé : (auteur) Integer ambiguity resolution (IAR) is one of the key techniques in GNSS high precise positioning. However, an overlooked incorrect integer ambiguity solution may cause severe biases in the positioning results. The optimal integer aperture estimator (IAE) has the largest possible success rate given a certain fail rate. An alternative approach that take advantage of ambiguity integer nature to minimize the solution’s mean squared error (MSE) is known as the best integer equivariant (BIE) estimator. Both of which are associated with the posterior probability of the GNSS integer ambiguity. It is therefore of great significance to calculate posterior probability precisely and efficiently. Due to the occurrence of infinite sums, practical calculation approaches approximate the exact value by neglecting sufficiently small terms in the sum. As a result, they can only produce posterior probability calculation result, information about the result’s accuracy cannot be produced. In this contribution, the value of the posterior probability is bounded from below and from above by dividing the infinite sum into two parts: the major finite part and the minor infinite part. They are calculated partly by enumeration and partly by algebraical bounding. The obtained upper and lower bounds are rigorous and in closed form, so that can be conveniently used. Based on both of the bounds, a method of posterior probability calculation with controllable accuracy is proposed. It not only produces posterior probability calculation result, but also calculation error, which is always smaller than the user-defined acceptable error. Numerical experiments have verified that the proposed approach has advantages on both controllable calculation accuracy and adjustable computational workload. Numéro de notice : A2022-607 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01633-w Date de publication en ligne : 10/08/2022 En ligne : https://doi.org/10.1007/s00190-022-01633-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101386 [article]

Smartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 303 - 310 Titre : Smartphone digital photography for fractional vegetation cover estimation Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Yonghua Qu, Auteur ; Aleixandre Verger, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 303 - 310 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] analyse comparative [Termes IGN] champ visuel [Termes IGN] couvert végétal [Termes IGN] erreur moyenne quadratique [Termes IGN] forêt alpestre [Termes IGN] image à haute résolution [Termes IGN] image hémisphérique [Termes IGN] objectif grand angulaire [Termes IGN] téléphone intelligent Résumé : (Auteur) Accurate ground measurements of fractional vegetation cover (FVC) are key for characterizing ecosystem functions and evaluating remote sensing products. The increasing performance of cameras equipped in smartphones opens new opportunities for extensive FVC measurement through citizen science initiatives. However, the wide field of view (FOV) of smartphone cameras constitutes a key source of uncertainty in the estimation of vegetation parameters, which has been largely ignored. We designed a practical method to characterize the FOV of smartphones and improve the FVC estimation. The method was assessed in a mountainous forest based on the comparison with in situ fisheye photographs. After the FOV correction, the agreement of smart-phone and fisheye FVC estimates highly improved: root-mean-square error (RMSE) of 0.103 compared to 0.242 of the original smartphone FVC estimates without considering the FOV effect, mean difference of 0.074 versus 0.213, and coefficient of determination R 2 of 0.719 versus 0.353. Smartphone cameras outperform traditional fisheye cameras: the overexposure and low vertical resolution of fisheye photographs introduced uncertainties in FVCestimation while the insensitivity to exposure and high spatial resolution of smartphone cameras make photograph acquisition and analysis more automatic and accurate. The smartphone FVCestimates highly agree with the GF-1 satellite product: RMSE = 0.066, bias = 0.007, and R 2 = 0.745. This study opens new perspectives for the validation of satellite products. Numéro de notice : A2022-527 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00038R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00038R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101375 [article]

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Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 1 - 18 Titre : Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation Type de document : Article/Communication Auteurs : Kathrin Maier, Auteur ; Andrea Nascetti, Auteur ; Ward van Pelt, Auteur ; Gunhild Rosqvist, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] bande infrarouge [Termes IGN] épaisseur [Termes IGN] erreur moyenne quadratique [Termes IGN] géoréférencement direct [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] manteau neigeux [Termes IGN] modèle numérique de surface [Termes IGN] photogrammétrie aérienne [Termes IGN] positionnement cinématique en temps réel [Termes IGN] qualité du modèle [Termes IGN] reconstruction 3D [Termes IGN] structure-from-motion [Termes IGN] Suède Résumé : (Auteur) More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high-resolution 3D snow-covered surface models (7 cm/pixel) of alpine areas up to eight hectares in a fast, reliable and affordable way. The test sites’ average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a significant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point measurements and regional-scale remote sensing and complements large-scale remote sensing data and snow model output with an adequate validation source. Numéro de notice : A2022-066 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.020 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99783 [article]

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A mean-squared-error condition for weighting ionospheric delays in GNSS baselines / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 11 (November 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 11 (November 2021) . - n° 118 Titre : A mean-squared-error condition for weighting ionospheric delays in GNSS baselines Type de document : Article/Communication Auteurs : Peter J.G. Teunissen, Auteur ; Amir Khodabandeh, Auteur Année de publication : 2021 Article en page(s) : n° 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] erreur moyenne quadratique [Termes IGN] ligne de base [Termes IGN] pondération [Termes IGN] propagation ionosphérique [Termes IGN] retard ionosphèrique [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Although ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify the needed weighting in dependence on ionospheric circumstances. The goal of the present contribution is therefore to develop and present the ionospheric conditions that need to be satisfied in order for the ionosphere-weighted solution to be mean squared error (MSE) superior to the ionosphere-float solution. When satisfied, the presented conditions guarantee from an MSE performance view, when (a) the ionosphere-fixed solution can be used, (b) the ionosphere-float solution must be used, or (c) an ionosphere-weighted solution can be used. Numéro de notice : A2021-777 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01569-7 Date de publication en ligne : 10/10/2021 En ligne : https://doi.org/10.1007/s00190-021-01569-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98840 [article]

Exploring fuzzy local spatial information algorithms for remote sensing image classification / Anjali Madhu in Remote sensing, vol 13 n° 20 (October-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 20 (October-2 2021) . - n° 4163 Titre : Exploring fuzzy local spatial information algorithms for remote sensing image classification Type de document : Article/Communication Auteurs : Anjali Madhu, Auteur ; Anil Kumar, Auteur ; Peng Jia, Auteur Année de publication : 2021 Article en page(s) : n° 4163 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] classification pixellaire [Termes IGN] distance euclidienne [Termes IGN] erreur moyenne quadratique [Termes IGN] Inde [Termes IGN] matrice d'erreur [Termes IGN] occupation du sol [Termes IGN] théorie des possibilités Résumé : (auteur) Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes. Numéro de notice : A2021-806 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204163 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.3390/rs13204163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98864 [article]

Observable quality assessment of broadband very long baseline interferometry system / Ming H. Xu in Journal of geodesy, vol 95 n° 5 (May 2021)  

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Integrity investigation of global ionospheric TEC maps for high-precision positioning / Jiaojiao Zhao in Journal of geodesy, vol 95 n° 3 (March 2021)  

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Adjusting the regular network of squares resolution to the digital terrain model surface shape / Dariusz Gościewski in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)  

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Dense stereo matching strategy for oblique images that considers the plane directions in urban areas / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)  

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Prognostic evaluation of the impact of restricted horizon visibility on the accuracy of position (coordinates) obtained with GNSS based on empirical data / Oleksandr Yanchuk in Geodesy and cartography, vol 46 n° 2 (July 2020)  

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Under-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Validation of Sentinel-3A SRAL coastal sea level data at high posting rate: 80 Hz / Ana Aldarias in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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Soil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Generation of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data / Behnaz Bigdeli in Survey review, vol 52 n° 371 (March 2020)  

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