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Combination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve / Michael Lechner in Remote sensing, vol 14 n° 11 (June-1 2022)
[article]
Titre : Combination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve Type de document : Article/Communication Auteurs : Michael Lechner, Auteur ; Alena Dostalova, Auteur ; Markus Hollaus, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2687 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] analyse harmonique
[Termes IGN] Autriche
[Termes IGN] biosphère
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] espèce végétale
[Termes IGN] feuillu
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] nébulosité
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] rapport signal sur bruit
[Termes IGN] réserve forestièreRésumé : (auteur) Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated. Numéro de notice : A2022-540 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14112687 Date de publication en ligne : 03/06/2022 En ligne : https://doi.org/10.3390/rs14112687 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101103
in Remote sensing > vol 14 n° 11 (June-1 2022) . - n° 2687[article]Effect of label noise in semantic segmentation of high resolution aerial images and height data / Arabinda Maiti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Effect of label noise in semantic segmentation of high resolution aerial images and height data Type de document : Article/Communication Auteurs : Arabinda Maiti, Auteur ; Sander J. Oude Elberink, Auteur ; M. George Vosselman, Auteur Année de publication : 2022 Article en page(s) : pp 275 - 282 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] bruit (théorie du signal)
[Termes IGN] données altimétriques
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image à très haute résolution
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] segmentation sémantiqueRésumé : (auteur) The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices. Numéro de notice : A2022-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-275-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-275-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100741
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 275 - 282[article]Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data Type de document : Article/Communication Auteurs : Lukas Winiwarter, Auteur ; Katharina Anders, Auteur ; Daniel Schröder, Auteur ; Bernhard Höfle, Auteur Année de publication : 2022 Article en page(s) : pp 79 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtre de Kalman
[Termes IGN] modèle de simulation
[Termes IGN] scène 3D
[Termes IGN] scène virtuelle
[Termes IGN] semis de points
[Termes IGN] Tyrol (Autriche)Résumé : (autuer) Virtual laser scanning (VLS) allows the generation of realistic point cloud data at a fraction of the costs required for real acquisitions. It also allows carrying out experiments that would not be feasible or even impossible in the real world, e.g., due to time constraints or when hardware does not exist. A critical part of a simulation is an adequate substitution of reality. In the case of VLS, this concerns the scanner, the laser-object interaction, and the scene. In this contribution, we present a method to recreate a realistic dynamic scene, where the surface changes over time. We first apply change detection and quantification on a real dataset of an erosion-affected high-mountain slope in Tyrol, Austria, acquired with permanent terrestrial laser scanning (TLS). Then, we model and extract the time series of a single change form, and transfer it to a virtual model scene. The benefit of such a transfer is that no physical modelling of the change processes is required. In our example, we use a Kalman filter with subsequent clustering to extract a set of erosion rills from a time series of high-resolution TLS data. The change magnitudes quantified at the locations of these rills are then transferred to a triangular mesh, representing the virtual scene. Subsequently, we apply VLS to investigate the detectability of such erosion rills from airborne laser scanning at multiple subsequent points in time. This enables us to test if, e.g., a certain flying altitude is appropriate in a disaster response setting for the detection of areas exposed to immediate danger. To ensure a successful transfer, the spatial resolution and the accuracy of the input dataset are much higher than the accuracy and resolution that are being simulated. Furthermore, the investigated change form is detected as significant in the input data. We, therefore, conclude the model of the dynamic scene derived from real TLS data to be an appropriate substitution for reality. Numéro de notice : A2022-437 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-79-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100746
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 79 - 86[article]An algorithm to assist the robust filter for tightly coupled RTK/INS navigation system / Zun Niu in Remote sensing, vol 14 n° 10 (May-2 2022)
[article]
Titre : An algorithm to assist the robust filter for tightly coupled RTK/INS navigation system Type de document : Article/Communication Auteurs : Zun Niu, Auteur ; Guangchen Li, Auteur ; Fugui Guo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] C++
[Termes IGN] centrale inertielle
[Termes IGN] erreur de positionnement
[Termes IGN] filtre de Kalman
[Termes IGN] implémentation (informatique)
[Termes IGN] matrice de covariance
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] précision du positionnement
[Termes IGN] rapport signal sur bruit
[Termes IGN] valeur aberranteRésumé : (auteur) The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers’ ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm’s accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub. Numéro de notice : A2022-401 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14102449 Date de publication en ligne : 20/05/2022 En ligne : https://doi.org/10.3390/rs14102449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100704
in Remote sensing > vol 14 n° 10 (May-2 2022) . - n° 2449[article]Regional ionospheric corrections for high accuracy GNSS positioning / Tam Dao in Remote sensing, vol 14 n° 10 (May-2 2022)
[article]
Titre : Regional ionospheric corrections for high accuracy GNSS positioning Type de document : Article/Communication Auteurs : Tam Dao, Auteur ; Ken Harima, Auteur ; Brett Anthony Carter, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2463 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] Australie
[Termes IGN] Continuously Operating Reference Station network
[Termes IGN] correction ionosphérique
[Termes IGN] modèle ionosphérique
[Termes IGN] positionnement par GNSS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard ionosphèriqueRésumé : (auteur) Centimetre-level accurate ionospheric corrections are required for a high accuracy and rapid convergence of Precise Point Positioning (PPP) GNSS positioning solutions. This research aims to evaluate the accuracy of a local/regional ionospheric delay model using a linear interpolation method across Australia. The accuracy of the ionospheric corrections is assessed as a function of both different latitudinal regions and the number and spatial density of GNSS Continuously Operating Reference Stations (CORSs). Our research shows that, for a local region of 5° latitude ×10° longitude in mid-latitude regions of Australia (~30° to 40°S) with approximately 15 CORS stations, ionospheric corrections with an accuracy of 5 cm can be obtained. In Victoria and New South Wales, where dense CORS networks exist (nominal spacing of ~100 km), the average ionospheric corrections accuracy can reach 2 cm. For sparse networks (nominal spacing of >200 km) at lower latitudes, the average accuracy of the ionospheric corrections is within the range of 8 to 15 cm; significant variations in the ionospheric errors of some specific satellite observations during certain periods were also found. In some regions such as Central Australia, where there are a limited number of CORSs, this model was impossible to use. On average, centimetre-level accurate ionospheric corrections can be achieved if there are sufficiently dense (i.e., nominal spacing of approximately 200 km) GNSS CORS networks in the region of interest. Based on the current availability of GNSS stations across Australia, we propose a set of 15 regions of different ionospheric delay accuracies with extents of 5° latitude ×10° longitude covering continental Australia. Numéro de notice : A2022-400 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14102463 Date de publication en ligne : 20/05/2022 En ligne : https://doi.org/10.3390/rs14102463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100703
in Remote sensing > vol 14 n° 10 (May-2 2022) . - n° 2463[article]Adaptive Kalman filter for real-time precise orbit determination of low earth orbit satellites based on pseudorange and epoch-differenced carrier-phase measurements / Min Li in Remote sensing, vol 14 n° 9 (May-1 2022)PermalinkDeep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)PermalinkCharacteristics of the BDS-3 multipath effect and mitigation methods using precise point positioning / Ran Lu in GPS solutions, vol 26 n° 2 (April 2022)PermalinkCoastal observation of sea surface tide and wave height using opportunity signal from Beidou GEO satellites: analysis and evaluation / Feng Wang in Journal of geodesy, vol 96 n° 4 (April 2022)PermalinkData assimilation of growing stock volume using a sequence of remote sensing data from different sensors / Niels Lindgren in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)PermalinkDetection and mitigation of GNSS spoofing via the pseudorange difference between epochs in a multicorrelator receiver / Xiangyong Shang in GPS solutions, vol 26 n° 2 (April 2022)PermalinkImproving the (re-)convergence of multi-GNSS real-time precise point positioning through regional between-satellite single-differenced ionospheric augmentation / Ahao Wang in GPS solutions, vol 26 n° 2 (April 2022)PermalinkOn enhanced PPP with single difference between-satellite ionospheric constraints / Yan Xiang in Navigation : journal of the Institute of navigation, vol 69 n° 1 (Spring 2022)PermalinkResults on GNSS spoofing mitigation using multiple receivers / Niklas Stenberg in Navigation : journal of the Institute of navigation, vol 69 n° 1 (Spring 2022)PermalinkValidating the impact of various ionosphere correction on mid to long baselines and point positioning using GPS dual-frequency receivers / Alaa A. Elghazouly in Journal of applied geodesy, vol 16 n° 2 (April 2022)PermalinkAn approach to extracting digital elevation model for undulating and hilly terrain using de-noised stereo images of Cartosat-1 sensor / Litesh Bopche in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkAssessing ZWD models in delay and height domains using data from stations in different climate regions / Thainara Munhoz Alexandre de Lima in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkFlood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (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)PermalinkA method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkA batch algorithm for GNSS carrier phase cycle slip correction / Brian Breitsch in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)PermalinkGCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)PermalinkGNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes / Omar Garcia Crespillo (2022)PermalinkMulti-frequency quadrifilar helix antennas for cm-accurate GNSS positioning / Lambert Wanninger in Journal of applied geodesy, vol 16 n° 1 (January 2022)PermalinkA multipath and thermal noise joint error characterization and exploitation for low-cost GNSS PVT estimators in urban environment / Eustachio Roberto Matera (2022)Permalink