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Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (October 2020)
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Titre : Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) Type de document : Article/Communication Auteurs : Mirza Razi Imam Baig, Auteur ; Ishita Afreen Ahmad, Auteur ; Mohammad Tayyab, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 361 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Andhra Pradesh (Inde ; état)
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] géomorphologie locale
[Termes IGN] image Landsat
[Termes IGN] pondération
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area. Numéro de notice : A2020-801 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1815839 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1080/19475683.2020.1815839 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96724
in Annals of GIS > vol 26 n° 4 (October 2020) . - pp 361 - 376[article]Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco / El Mahdi El Khalk in Natural Hazards and Earth System Sciences, vol 20 n° 10 (October 2020)
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Titre : Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco Type de document : Article/Communication Auteurs : El Mahdi El Khalk, Auteur ; Yves Tramblay, Auteur ; Christian Massari, Auteur Année de publication : 2020 Article en page(s) : pp 2591 - 2607 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Advanced scatterometer
[Termes IGN] Atlas marocain
[Termes IGN] bassin hydrographique
[Termes IGN] crue
[Termes IGN] humidité du sol
[Termes IGN] image SMOS
[Termes IGN] inondation
[Termes IGN] Maroc
[Termes IGN] modèle hydrographique
[Termes IGN] modélisation
[Termes IGN] variation saisonnière
[Termes IGN] zone semi-arideRésumé : (auteur) The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking. Numéro de notice : A2020-610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/nhess-20-2591-2020 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.5194/nhess-20-2591-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95974
in Natural Hazards and Earth System Sciences > vol 20 n° 10 (October 2020) . - pp 2591 - 2607[article]Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])
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Titre : Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] canopée
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] séparateur à vaste marge
[Termes IGN] spectroradiomètreRésumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 Date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967
in Geocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020101 RAB Revue Centre de documentation En réserve L003 Disponible Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
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Titre : Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution Type de document : Article/Communication Auteurs : Vitor Martins, Auteur ; Amy L. Kaleita, Auteur ; Brian K. Gelder, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 56 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données multiéchelles
[Termes IGN] hétérogénéité environnementale
[Termes IGN] image à haute résolution
[Termes IGN] occupation du sol
[Termes IGN] reconnaissance d'objets
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] squelettisationRésumé : (auteur) Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (“medial axis”) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN16 or CNN256, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution. Numéro de notice : A2020-634 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.004 Date de publication en ligne : 13/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96057
in ISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 56 - 73[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal / Raghu Nadimpalli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
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Titre : Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal Type de document : Article/Communication Auteurs : Raghu Nadimpalli, Auteur ; Akhil Srivastava, Auteur ; V. S. Prasad, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6945 - 6957 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bengale, golfe du
[Termes IGN] cyclone
[Termes IGN] image INSAT-VHRR
[Termes IGN] interpolation
[Termes IGN] matrice de covariance
[Termes IGN] modèle de transfert radiatif
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] radiance
[Termes IGN] zone intertropicaleRésumé : (auteur) This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin. Numéro de notice : A2020-587 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978211 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95915
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6945 - 6957[article]Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)
PermalinkMultiview automatic target recognition for infrared imagery using collaborative sparse priors / Xuelu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
PermalinkA spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications / T. Chakraborty in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
PermalinkSpatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)
PermalinkWide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October-1 2020)
PermalinkBackground tropospheric delay in geosynchronous synthetic aperture radar / Dexin Li in Remote sensing, vol 12 n° 18 (September-2 2020)
PermalinkApplication of 30-meter global digital elevation models for compensating rational polynomial coefficients biases / Amin Alizadeh Naeini in Geocarto international, vol 35 n° 12 ([01/09/2020])
PermalinkApplying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)
PermalinkAssessing local trends in indicators of ecosystem services with a time series of forest resource maps / Matti Katila in Silva fennica, vol 54 n° 4 (September 2020)
PermalinkL-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])
PermalinkCombining optical and radar satellite image time series to map natural vegetation: savannas as an example / Maylis Lopes in Remote sensing in ecology and conservation, vol 6 n° 3 (September 2020)
PermalinkComparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)
PermalinkDeriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkHeliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkIlluminating the spatio-temporal evolution of the 2008–2009 Qaidam earthquake sequence with the joint use of Insar time series and teleseismic data / Simon Daout in Remote sensing, vol 12 n° 17 (September-1 2020)
PermalinkMapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkMonitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)
PermalinkA novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images / Heng Lyu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkSemi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkA spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)
PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkShoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)
PermalinkAccuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkCan SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkConjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data / Bingquan Li in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)
PermalinkDevelopment and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkEstimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data / Kaya Kanemaru in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)
PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkLanduse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkOn-Orbit Calibration of Terra MODIS VIS Bands Using Polarization-Corrected Desert Observations / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)
PermalinkRecent changes in two outlet glaciers in the Antarctic Peninsula using multi-temporal Landsat and Sentinel-1 data / Carolina L. Simões in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkTowards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkA worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkCartographie des surfaces pastorales à l’aide des données Sentinel 2 L3A et des données ouvertes : Promesses et réalités / Urcel Kalenga Tshingomba in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)
PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)
PermalinkCross-calibration of MODIS reflective solar bands with Sentinel 2A/2B MSI instruments / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkImproved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)
PermalinkMapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkA simple distributed water balance model for an urbanized river basin using remote sensing and GIS techniques / Olutoyin Adeola Fashae in Geocarto international, vol 35 n° 9 ([01/07/2020])
PermalinkALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction / Litu Rout in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
PermalinkAn integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
PermalinkAqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy / Hrishikesh Kumar in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)
PermalinkCoastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)
PermalinkDiscriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
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