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Quantifying coherence between TDM90, SRTM90 and ASTER90 / Umut Gunes Sefercik in Geocarto international, vol 36 n° 15 ([15/08/2021])
[article]
Titre : Quantifying coherence between TDM90, SRTM90 and ASTER90 Type de document : Article/Communication Auteurs : Umut Gunes Sefercik, Auteur Année de publication : 2021 Article en page(s) : pp 1752 -1767 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse comparative
[Termes IGN] cohérence des données
[Termes IGN] contour
[Termes IGN] données topographiques
[Termes IGN] MNS ASTER
[Termes IGN] MNS SRTM
[Termes IGN] MNS TerraSAR & TanDEM-X
[Termes IGN] pente
[Termes IGN] TurquieRésumé : (auteur) German Aerospace Centre released TanDEM-X 90 m (TDM90) global digital elevation model (GDEM) as freely available in October, 2018 and declared that it describes all Earth’s landmasses pole to pole with 1 m absolute height accuracy which could not been achieved by previous space-borne GDEMs. In this study, the coherence level of TDM90 with SRTM90 and ASTER90 were comprehensively analysed by visual and statistical comparison approaches in two study areas including different topographic characteristics. In visual approaches, colour-scaled coherence maps, contour maps, aspects depending on ascending and descending flying orbits and outlier illustrations were generated and interpreted. In statistical approaches, horizontal and vertical absolute and relative geolocation disparities and frequency distributions of height differences were presented. The results demonstrated that the terrain slope has a great impact on the coherence levels between TDM90 and compared GDEMs. Overall, TDM90 is more coherent with SRTM90 against ASTER90 except clear dissimilarity problems in strip border lines. Numéro de notice : A2021-591 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1659425 Date de publication en ligne : 02/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1659425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98211
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1752 -1767[article]Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])
[article]
Titre : Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Ajay K. Maurya, Auteur ; Onkar Dikshit, Auteur Année de publication : 2021 Article en page(s) : pp 1709 - 1731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] bande spectrale
[Termes IGN] classification floue
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par nuées dynamiques
[Termes IGN] distribution spatiale
[Termes IGN] entropie
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] Inde
[Termes IGN] manteau neigeux
[Termes IGN] neige
[Termes IGN] réflectance spectraleRésumé : (auteur) Information on the spatial and temporal extent of snow cover distribution is a significant input in hydrological processes and climate models. Although hyperspectral remote sensing provides significant opportunities in the assessment of land cover, the applications of such data are limited in the snow-covered alpine regions. A major issue with hyperspectral data is the larger dimensionality. Feature selection methods are often used to derive the most informative subset of bands from the hyperspectral data. In this study, a band selection technique is proposed which utilizes the mutual information (MI) between hyperspectral bands and a reference band. The first principal component of the hyperspectral data is selected as the reference band. Two variants of this approach are proposed involving preclustering of bands using: (1) the k-means and (2) the fuzzy k-means algorithms. The MI is derived from weighted entropy of the hyperspectral band and the reference band. The weights are computed from the cluster distance ratio and the cluster membership function for the k-means and fuzzy k-means algorithm, respectively. The selected bands were classified using random forest classifier. The proposed methods are evaluated with four datasets, two Hyperion datasets corresponding to the geographical locations of Dhundi and Solang in India, corresponding to snow covered terrain and two benchmark AVIRIS datasets of Indian Pines and Salinas. The average classification accuracy (0.995 and 0.721 for Dhundi and Solang datasets, respectively) for the proposed approach were observed to be better as compared with those from other state of the art techniques. Numéro de notice : A2021-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1665717 Date de publication en ligne : 18/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1665717 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98183
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1709 - 1731[article]Calibration of the process-based model 3-PG for major central European tree species / David I. Forrester in European Journal of Forest Research, vol 140 n° 4 (August 2021)
[article]
Titre : Calibration of the process-based model 3-PG for major central European tree species Type de document : Article/Communication Auteurs : David I. Forrester, Auteur ; Martina Lena Hobi, Auteur ; Amanda S. Mathys, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 847 - 868 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] estimation bayesienne
[Termes IGN] étalonnage de modèle
[Termes IGN] Europe centrale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de croissance végétale
[Termes IGN] modélisation de la forêt
[Termes IGN] peuplement mélangé
[Termes IGN] régression
[Termes IGN] Suisse
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Process-based forest models are important tools for predicting forest growth and their vulnerability to factors such as climate change or responses to management. One of the most widely used stand-level process-based models is the 3-PG model (Physiological Processes Predicting Growth), which is used for applications including estimating wood production, carbon budgets, water balance and susceptibility to climate change. Few 3-PG parameter sets are available for central European species and even fewer are appropriate for mixed-species forests. Here we estimated 3-PG parameters for twelve major central European tree species using 1418 long-term permanent forest monitoring plots from managed forests, 297 from un-managed forest reserves and 784 Swiss National Forest Inventory plots. A literature review of tree physiological characteristics, as well as regression analyses and Bayesian inference, were used to calculate the 3-PG parameters. The Swiss-wide calibration, based on monospecific plots, showed a robust performance in predicting forest stocks such as stem, foliage and root biomass. The plots used to inform the Bayesian calibration resulted in posterior ranges of the calibrated parameters that were, on average, 69% of the prior range. The bias of stem, foliage and root biomass predictions was generally less than 20%, and less than 10% for several species. The parameter sets also provided reliable predictions of biomass and mean tree sizes in mixed-species forests. Given that the information sources used to develop the parameters included a wide range of climatic, edaphic and management conditions and long time spans (from 1930 to present), these species parameters for 3-PG are likely to be appropriate for most central European forests and conditions. Numéro de notice : A2021-717 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01370-3 Date de publication en ligne : 18/03/2021 En ligne : https://doi.org/10.1007/s10342-021-01370-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98630
in European Journal of Forest Research > vol 140 n° 4 (August 2021) . - pp 847 - 868[article]Deep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)
[article]
Titre : Deep learning-based image de-raining using discrete Fourier transformation Type de document : Article/Communication Auteurs : Prasen Kumar Sharma, Auteur ; Sathisha Basavaraju, Auteur ; Arijit Sur, Auteur Année de publication : 2021 Article en page(s) : pp 2083 - 2096 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] classification par réseau neuronal convolutif
[Termes IGN] décomposition d'image
[Termes IGN] filtrage du bruit
[Termes IGN] pluie
[Termes IGN] transformation de FourierRésumé : (auteur) Single image rain streak removal is a well-explored topic in the field of computer vision. The de-raining problem is modeled as an image decomposition task where a rainy image is decomposed into rain-free background image and rain streek map. Unlike most of the existing de-raining methods, this paper attempts to decompose the rainy image in the frequency domain. The idea is inspired by pseudo-periodic characteristics of the noise signal (here the rain streaks) which leave some traces in the frequency domain, and the same can be utilized to predict the noise signal. In this paper, a deep learning-based rain streak prediction model is proposed which learns in discrete Fourier transform Oppenheim and Schafer (Discrete-Time Signal Processing, Prentice Hall, Upper Saddle River, 1989) domain. To the best of our knowledge, this is the first approach where compressed domain coefficients are directly used as input to a deep convolutional neural network. The proposed model has been tested on publicly available synthetic datasets Fu et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.186, Yang et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.183), Yeh et al. (in: 2015 IEEE International Conference on Consumer Electronics-Taiwan, 2015. https://doi.org/10.1109/ICCE-TW.2015.7216999) and results are found to be comparable with the state of the art methods in the spatial domain. The presented analysis and study have an obvious indication to extend transform domain input to train the deep learning architecture especially image de-noising like problems. Numéro de notice : A2021-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01971-w Date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1007/s00371-020-01971-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98226
in The Visual Computer > vol 37 n° 8 (August 2021) . - pp 2083 - 2096[article]Establishing vertical separation models for vulnerable coastlines in developing territories / Cassandra Nanlal in Marine geodesy, vol 44 n° 5 (September 2021)
[article]
Titre : Establishing vertical separation models for vulnerable coastlines in developing territories Type de document : Article/Communication Auteurs : Cassandra Nanlal, Auteur ; Keith Miller, Auteur ; Dexter Davis, Auteur ; Michael Sutherland, Auteur Année de publication : 2021 Article en page(s) : pp 387 - 407 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] aménagement du littoral
[Termes IGN] Amérique du sud
[Termes IGN] changement climatique
[Termes IGN] données altimétriques
[Termes IGN] géodésie physique
[Termes IGN] golfe
[Termes IGN] hauteurs de mer
[Termes IGN] niveau de la mer
[Termes IGN] simulation hydrodynamique
[Termes IGN] système de référence altimétrique
[Termes IGN] système de référence local
[Termes IGN] trait de côte
[Termes IGN] vulnérabilitéRésumé : (Auteur) Vertical separation models are valuable for coastal zone management and protection against the effects of climate change. To date, the development of such models has been undertaken in areas where long-term sea level measurements exist and there are resources for extensive offshore bathymetric and Global Navigation Satellite Systems surveys. Many small island developing states and other resource constrained territories host vulnerable coastal zones and would benefit from such models, however, financial constraints and data sparsity make it difficult. This article describes the establishment of a vertical separation model using an amalgamation of long- and short-term sea level measurements with hydrodynamic modeling. With existing vertical separations at only two coastal points for comparison, the model was designed to include a tidal prediction element which allowed for validation against sparse independently observed sea levels. Considering that unmodeled influences on sea levels in the study area can exceed 0.2 m at times, the method was tested against independently observed sea levels and can be considered successful with variances in the range of 1.3–4.5% of the average tidal range for the study area. This research provides the means of addressing a significant need in developing territories where long-term sea level records are unavailable and resource deficiencies exist. Numéro de notice : A2021-576 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1844825 Date de publication en ligne : 02/12/2020 En ligne : https://doi.org/10.1080/01490419.2020.1844825 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98227
in Marine geodesy > vol 44 n° 5 (September 2021) . - pp 387 - 407[article]Estimation of code observation-specific biases (OSBs) for the modernized multi-frequency and multi-GNSS signals: an undifferenced and uncombined approach / Teng Liu in Journal of geodesy, vol 95 n° 8 (August 2021)PermalinkPermalinkA high-efficiency global model of optimization design of impervious surfaces for alleviating urban waterlogging in urban renewal / Huafei Yu in Transactions in GIS, Vol 25 n° 4 (August 2021)PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkMeasuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)PermalinkRandom forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkRelative influence of stand and site factors on aboveground live-tree carbon sequestration and mortality in managed and unmanaged forests / Christel C. Kern in Forest ecology and management, vol 493 (August-1 2021)PermalinkRemote sensing method for extracting topographic information on tidal flats using spatial distribution features / Yang Lijun in Marine geodesy, vol 44 n° 5 (September 2021)PermalinkShore zone classification from ICESat-2 data over Saint Lawrence Island / Huan Xie in Marine geodesy, vol 44 n° 5 (September 2021)Permalink