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Monitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)    

Public Titre : Monitoring grassland dynamics by exploiting multi-modal satellite image time series Titre original : Suivi de la dynamique des prairies permanentes par analyse des séries temporelles multi-modales Type de document : Thèse/HDR Auteurs : Anatol Garioud , Auteur ; Clément Mallet , Directeur de thèse ; Silvia Valero, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse présentée et soutenue en vue de l'obtention du Doctorat de l'Université Gustave Eiffel, Spécialité Sciences et Technologies de l'Information Géographique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] analyse multivariée [Termes IGN] apprentissage profond [Termes IGN] classification par Perceptron multicouche [Termes IGN] données auxiliaires [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Mâcon [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] prairie [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] seuillage d'image [Termes IGN] superpixel [Termes IGN] surveillance agricole [Termes IGN] Toulouse Index. décimale : THESE Thèses et HDR Résumé : (Auteur) The vast grassland surfaces as well as the growing recognition of the ecosystem services thez provide have revealed urgent needs for their conservation and sutainable management. Despite the acknowledged importance of grassland management practices, there are currently no large-scale efforts reporting on their frequency and nature. Satellite remote sensing time series appear to be a suitable tool for efficient grassland monitoring and allow synoptic and regular analysis. The research conducted in this PhD aims to develop methods for the detection of grassland management practices from complementary optical and SAR multivariate time series. Advances in deep learning are employed to regress multivariate SAR time series and contextual knowledge towards optical NDVI. Resulting gap-free time series are used to efficiently explore methods aiming to detect vegetation status changes related to management practices on grasslands. Note de contenu : INTRODUCTION 1. Grasslands and remote sensing: context, diversity and challenges 1.1 Definition, extent and importance of grasslands 1.2 Earth observation from space: principles and applications over grasslands 1.3 Problem statement and objectives 1.4 Outline of the manuscript 2. Study areas and datasets 2.1 Study areas 2.2 Satellite data 2.3 Reference and ancillary datasets 2.4 Feature derived from sentinel images for grassland monitoring 2.5 Description of the feature engineering steps 2.6 Exploring the relationships between derived satellite features 2.7 Concluding remarks HIGH-TEMPORAL SAMPLED TIME-SERIES 3. Sentinels regression for vegetation monitoring 3.1 Monitoring vegetation through optical-SAR synergy 3.2 Retrieving missing data in optical time series 3.3 SenRVM: a deep learning-based regression framework 3.4 Concluding remarks 4. Outcomes of the SenRVM approach 4.1 Experimental design for training and evaluating SenRVM models 4.2 Assessment of SenRVM predictions 4.3 Empirical analysis of the SenRVM results 4.4 Generalization capabilities of single-class grassland SenRVM models 4.5 Further post-processing of SenRVM results 4.6 Concluding remarks MONITORING GRASSLANDS 5. Detecting and quantifying grassland management practices 5.1 Challenges and related work 5.2 The proposed methodology 5.3 Description of validation data 5.4 Experimental setup 5.5 Assessment of the proposed method 5.6 Potential outcomes 5.7 Concluding remarks GENERAL CONCLUSION 6. Conclusion and perspectives 6.1 Summary 6.2 Perspectives Numéro de notice : 26831 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l'Information Géographique : Gustave Eiffel : 2022 Organisme de stage : LASTIG (IGN) nature-HAL : Thèse DOI : sans En ligne : https://www.theses.fr/s208897 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100728

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A PCA-PD fusion method for change detection in remote sensing multi temporal images / Soltana Achour in Geocarto international, vol 37 n° 1 ([01/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 1 [01/01/2022] . - pp 196 - 213 Titre : A PCA-PD fusion method for change detection in remote sensing multi temporal images Type de document : Article/Communication Auteurs : Soltana Achour, Auteur ; Miloud Chikr Elmezouar, Auteur ; Nasreddine Taleb, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 196 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] fusion de données [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image panchromatique [Termes IGN] méthode statistique [Termes IGN] seuillage d'image Résumé : (auteur) In remote sensing, for applications as environment monitoring, change detection based on image processing is one of the most important techniques. To reach high performance various techniques of fusion are exploited using a combination of multi-temporal, multispectral and panchromatic satellite images. A solution for handling such kind of images holds when using some simple statistical methods like the Percent Difference (PD) technique as well as the Principal Component Analysis (PCA) one. In this paper, an automatic change detection method issued from the two previous techniques is proposed and applied on multispectral and panchromatic images captured by a high resolution optical satellite. This approach is characterized by two aspects: the first one consists of the fusion of the different data and the second one performs the detection of the changes for the resulting images. The experimental results show the reasonable quantitative performance and the effectiveness of the proposed method for change detection, consisting of an automatic extraction of most of change information as well as the obtention of better results for most precision metrics consisting of an overall accuracy of up to 91% and a Kappa coefficient of up to 66%, comparing to those obtained using the simple PD and PCA techniques. Numéro de notice : A2022-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1713228 Date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1713228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99441 [article]

Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5494 - 5514 Titre : Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images Type de document : Article/Communication Auteurs : Donato Amitrano, Auteur ; Raffaella Guida, Auteur ; Pasquale Lervolino, Auteur Année de publication : 2021 Article en page(s) : pp 5494 - 5514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse forestière [Termes IGN] canopée [Termes IGN] changement d'occupation du sol [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image RVB [Termes IGN] image Sentinel-SAR [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] segmentation d'image [Termes IGN] seuillage d'image [Termes IGN] texture d'image Résumé : (auteur) Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. Numéro de notice : A2021-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029841 Date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97978 [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 IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Groenland [Termes IGN] hydrocarbure [Termes IGN] iceberg [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] navire [Termes IGN] océan [Termes IGN] seuillage d'image [Termes 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]

Integrated Kalman filter of accurate ranging and tracking with wideband radar / Shaopeng Wei in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8395 - 8411 Titre : Integrated Kalman filter of accurate ranging and tracking with wideband radar Type de document : Article/Communication Auteurs : Shaopeng Wei, Auteur ; Lei Zhang, Auteur ; Hongwei Liu, Auteur Année de publication : 2020 Article en page(s) : pp 8395 - 8411 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande spectrale [Termes IGN] filtrage bayésien [Termes IGN] filtre de Kalman [Termes IGN] inférence statistique [Termes IGN] largeur de bande [Termes IGN] phase [Termes IGN] poursuite de cible [Termes IGN] seuillage d'image [Termes IGN] signal Résumé : (auteur) Accurate ranging and wideband tracking are treated as two independent and separate processes in traditional radar systems. As a result, limited by low data rate due to nonsequential processing, accurate ranging usually performs low efficiency in practical application. Similarly, without applying accurate ranging, the data after thresholding and clustering are used in wideband tracking, leading to a significant decrease in tracking accuracy. In this article, an integrated Kalman filter of accurate ranging and tracking is proposed using methods of phase-derived-ranging and Bayesian inference in wideband radar. Besides the motion state, in this integrated Kalman filter, the complex-valued high-resolution range profile (HRRP) is also introduced as a reference signal by coherent integration in a sliding window, which incorporates target’s scattering distribution and phase characteristics. Corresponding kinetic equations are derived to predict the motion state and the reference signal in the next moment. A ranging process is constructed based on the received signal and the predicted reference signal in order to estimate innovation using methods of phase-derived-ranging and Bayesian inference, and a sequential update for motion state can be accomplished with the Kalman filter as well. In every recursion, the complex-valued reference signal is also updated by coherently integrating the latest pulses. The integrated Kalman filter takes full use of high range resolution and phase information, improving both efficiency and precision compared with conventional approaches of ranging and wideband tracking. Implemented in a sequential manner, the integrated Kalman filter can be applied in a real-time application, realizing simultaneous ranging with high precision and wideband tracking. Finally, simulated and real-measured experiments confirm the remarkable performance. Numéro de notice : A2020-740 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987854 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987854 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96367 [article]

Efficient match pair selection for oblique UAV images based on adaptive vocabulary tree / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

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A novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)  

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Détection et vectorisation automatiqued’objets linéaires dans des nuages de points de voirie / Etienne Barçon (2020)  

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Extraction de caractéristiques sur des images acquises en contexte mobile / Yannick Faula (2020)  

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Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])  

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Une nouvelle méthode de vectorisation du cadastre ancien / Antony Chalais in Géomatique expert, n° 129 (août - septembre 2019)

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Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data / Prateek Verma in Geocarto international, vol 34 n° 10 ([15/07/2019])  

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A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery / Tingting Wu in Advances in space research, vol 64 n°1 (1 July 2019)  

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Potentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)  

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Denoising of natural images through robust wavelet thresholding and genetic programming / Asem Khmag in The Visual Computer, vol 33 n°9 (September 2017)  

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