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Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)  

Public [article]  inRemote sensing of environment > Vol 256 (April 2020) . - n° 112318 Titre : Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine Type de document : Article/Communication Auteurs : Dong Liang, Auteur ; Huadong Guo, Auteur ; Lu Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112318 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] albedo [Termes descripteurs IGN] Antarctique [Termes descripteurs IGN] calotte glaciaire [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] fonte des glaces [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] montée du niveau de la mer [Termes descripteurs IGN] série temporelle Résumé : (auteur) The Antarctic ice sheet is an important mass of glacier ice. It is particularly sensitive to climate change, and the flow of Antarctica's inland glaciers into the sea, accelerated by collapsing ice shelves, threatens global sea level rise. The amount of snowmelt on the surface of the ice sheet is an important metric for accurately assessing surface material loss and albedo change, which affect the stability of the ice sheet. This study proposes a framework for quickly extracting time-series freeze-thaw information at the continental scale and 40 m resolution by taking advantage of the huge amount of synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites over the Antarctic, available for rapid processing on Google Earth Engine. Co-orbit normalization is used in the proposed framework to establish a unified standard of judgement by reducing the variations in the backscattering coefficient introduced by observation geometry, terrain fluctuations, and melt conditions between images acquired at different times. We implemented the framework to produce a massive dataset of both monthly freeze-thaw information over the Antarctic and higher temporal resolution freeze-thaw information for the Larsen C ice shelf from 2015 to 2019, with overall accuracies of 93% verified by a manual visual interpretation method and 84% evaluated from automatic weather station temperatures. Due to its effectiveness and robustness, the framework can be used to analyse the spatiotemporal distribution of snowmelt, the change in melt area, and anomalous melt events in Antarctica, especially those in Larsen C caused by foehn wind. Numéro de notice : A2021-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112318 date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112318 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97117 [article]

Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 247 - 259 Titre : Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery Type de document : Article/Communication Auteurs : Nazanin Asadi, Auteur ; K. Andrea Scott, Auteur ; Alexander S. Komarov, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 247 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] assimilation des données [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] modèle d'incertitude [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] pondération [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] régression logistique [Termes descripteurs IGN] réseau neuronal artificiel Résumé : (auteur) Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular importance. Although several automated approaches have been proposed, none look at the impact of including an estimate of uncertainty of the model parameters and input features on the classification output. This article uses an established database of SAR image features to train a multilayer perceptron (MLP) neural network to classify pixel locations as either ice, water, or unknown. The classification accuracies are benchmarked using a recently developed logistic regression approach for the same database. The two methods are found to be comparable. The MLP approach is then enhanced to allow uncertainty to be estimated at each pixel location. Following methods proposed in the deep learning community, two kinds of uncertainty are considered. The first, epistemic uncertainty, is that due to uncertainty in the MLP weights. The second kind of uncertainty, aleatoric uncertainty, is that which cannot be explained by the model, and is therefore associated with the input data. It is found that including these uncertainties in the MLP models reduces their accuracies slightly, but also reduces misclassification rates. This is of particular importance for data assimilation applications, where misclassifications could severely degrade the analysis. Numéro de notice : A2021-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992454 date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992454 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96735 [article]

Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 758 Titre : Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination Type de document : Article/Communication Auteurs : Frederik Hass, Auteur ; Jamal Jokar Arsanjani, Auteur Année de publication : 2020 Article en page(s) : n° 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] Groenland [Termes descripteurs IGN] hydrocarbure [Termes descripteurs IGN] iceberg [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] navire [Termes descripteurs IGN] océan [Termes descripteurs IGN] seuillage d'image [Termes descripteurs IGN] trafic maritime Résumé : (auteur) Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. Numéro de notice : A2020-808 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120758 date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.3390/ijgi9120758 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96953 [article]

The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran / Hojatolah Ganjkhanlo in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1769 - 1782 Titre : The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran Type de document : Article/Communication Auteurs : Hojatolah Ganjkhanlo, Auteur ; Mehdi Vafakhah, Auteur ; Hossein Zeinivand, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1769 - 1782 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] classification hypercube [Termes descripteurs IGN] eau de fonte [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] épaisseur [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] Iran [Termes descripteurs IGN] krigeage [Termes descripteurs IGN] manteau neigeux [Termes descripteurs IGN] neige [Termes descripteurs IGN] précision de l'estimation [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) The aim of this study is to compare the effect of two sampling patterns: systematic sampling and Latin hypercube sampling (LHS), on estimation precision of snow water equivalent (SWE), and also comparing different geostatistics methods of kriging, cokriging and radial basin functions for mapping SWE. To achieve the study purpose, the semi-arid mountainous watershed of Sohrevard in Zanjan Province of Iran was selected. Snow depth in 150 points with systematic sampling and 150 points with LHS sampling and snow density in 18 points were randomly measured. In addition, SWE was calculated in the study area, and its map was derived based on both the sampling methods using geostatistical techniques. The results showed that the accuracy of the SWE map using LHS was higher than systematic sampling. According to the most statistical indicators, in both methods of sampling, accuracy of mapping using regular spline was better than other methods. Numéro de notice : A2020-725 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581267 date de publication en ligne : 03/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581267 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96328 [article]

Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 174-191 Titre : Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection Type de document : Article/Communication Auteurs : Chandi Witharana, Auteur ; Md Abul Ehsan Bhuiyan, Auteur ; Anna K. Liljedahl, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 174-191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Arctique [Termes descripteurs IGN] artefact [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] glace [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] pergélisol [Termes descripteurs IGN] texture d'image Résumé : (Auteur) The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region. Numéro de notice : A2020-705 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.010 date de publication en ligne : 01/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96232 [article]

Geostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier / Simone Baffelli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

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River ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

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Recent 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])  

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Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF) / Su Shu in Marine geodesy, Vol 43 n° 3 (May 2020)  

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Assessment of the Baspa basin glaciers mass budget using different remote sensing methods and modeling techniques / Vinay Kumar Gaddam in Geocarto international, vol 35 n° 3 ([01/03/2020])  

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Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products / Mengmeng Li in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

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Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Impact of GPS processing on the estimation of snow water equivalent using refracted GPS signals / Ladina Steiner in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)  

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Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data / Hua Su in Remote sensing, Vol 11 n° 20 (October 2019)  

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