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Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission / Nicolas Gasnier (2022)
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Titre : Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission Type de document : Thèse/HDR Auteurs : Nicolas Gasnier, Auteur ; Florence Tupin, Directeur de thèse ; Loïc Denis, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2022 Importance : 213 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat présentée à l’Institut Polytechnique de Paris, spécialité ImagesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] base de données localisées
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données hydrographiques
[Termes IGN] hauteurs de mer
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] image SWOT
[Termes IGN] lac
[Termes IGN] rivière
[Termes IGN] série temporelle
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it. Note de contenu : 1. Introduction
1.1 Context
1.2 Contributions
1.3 Organization of the manuscript
I BACKGROUND ON SAR REMOTE SENSING AND WATER SURFACE MONITORING WITH SAR IMAGES
2. SAR images
2.1 Physics and statistics of SAR images
2.2 The SWOT mission
2.3 Sentinel-1
3. SAR water detection and hydrological prior
3.1 Water detection in SAR images
3.2 SWOT processing and products
3.3 Prior water masks and databases
4. Methodological background
4.1 Markov random fields
4.2 Variational methods for image denoising
PROPOSED APPROACHES
5. Guided extraction of narrow rivers on SAR images using an exogenous river database
5.1 Introduction
5.2 Proposed river segmentation pipeline
5.3 Experimental results
5.4 Conclusion
6. Adaptation of the GrabCut method to SAR images: lake detection from a priori polygon
6.1 Single-date GrabCut method for lake detection from a priori polygon
6.2 Multitemporal and multi-sensor adaptations of the method
6.3 2D+T GrabCut of SAR images with temporal regularization for lake detection within an a priori mask
6.4 Joint 2D+T segmentation of SAR and optical images
7. Denoising of the temporal geometric mean
7.1 Introduction
7.2 Statistics of the temporal geometric mean of SAR intensities
7.3 Denoising method
7.4 Experiments
7.5 Application to change detection
7.6 Application to ratio-based denoising of single SAR images within a time series
7.7 Conclusion
8 Conclusion and perspectivesNuméro de notice : 26762 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Images : Palaiseau : 2022 Organisme de stage : Télécom Paris nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/02/2022 En ligne : https://tel.hal.science/tel-03578831/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99823
Titre : Variations de volume des lacs pour l'analyse climatique : Améliorer la connaissance de la quantité d’eau des lacs et leur variation à partir de données satellitaires Type de document : Mémoire Auteurs : Iris Lucas, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de fin d'étude, cycle des Ingénieurs diplômés de l’ENSG 3ème année, Spécialité PPMDLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin hydrographique
[Termes IGN] Canada
[Termes IGN] carte hypsométrique
[Termes IGN] Champagne (province, comté)
[Termes IGN] données altimétriques
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Landsat-8
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] lac
[Termes IGN] méthode robuste
[Termes IGN] modèle de Gauss-Helmert
[Termes IGN] Ransac (algorithme)
[Termes IGN] régression
[Termes IGN] variation temporelle
[Termes IGN] volume d'eauIndex. décimale : MPPMD Mémoires du mastère spécialisé Photogrammétrie, Positionnement et Mesures de Déformation Résumé : (auteur) La ressource en eau douce est limitée, son étude fait partie des axes majeurs des études environnementales. C’est au sein de la cellule hydrologie continentale de CLS, pour le compte d’Apside que je me suis penchée sur cette question, appliquant les savoirs acquis en géomatique durant mes années à l’ENSG. L’objectif de ce stage est d’améliorer la connaissance de la quantité d’eau des lacs et leur variation à partir de données satellitaires. Ce savoir pourra être appliqué dans divers projets sur l’étude des lacs à CLS. Etudier les variations de volume nécessite l’utilisation de surfaces d’eau que l’on peut extraire par imagerie satellitaire (Sentinel-2, Landsat-8) et hauteurs d’eau provenant de satellites altimétriques (accessibles sur la plateforme Hydroweb). Pour ce faire, j’ai développé un algorithme d’extraction de surfaces d’eau par télédétection optique, puis développé une méthode d’estimation robuste pour dégager une courbe hypsométrique. Grâce à cette courbe, j’ai pu déterminer des variations de volumes pour divers bassins. Ce rapport détaille le processus développé, la méthodologie suivie et les éventuelles pistes d’amélioration possibles. Note de contenu :
1- Introduction
2- Extraire les données de surfaces d’eau
3- Extraire le profil des lacs : la courbe hypsométrique
4- Dernière étape de la chaine : génération des variations de volume
5- ConclusionNuméro de notice : 24053 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de fin d'études IT Organisme de stage : Apside Toulouse Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101951 Documents numériques
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Titre : Vegetation index and dynamics Type de document : Monographie Auteurs : Eusebio Cano Carmona, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2022 Importance : 350 p. ISBN/ISSN/EAN : 978-1-83969-385-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatiale
[Termes IGN] analyse spectrale
[Termes IGN] Autocad Map
[Termes IGN] carte de la végétation
[Termes IGN] changement d'utilisation du sol
[Termes IGN] Colombie
[Termes IGN] couvert forestier
[Termes IGN] dynamique de la végétation
[Termes IGN] écosystème urbain
[Termes IGN] flore endémique
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] Inde
[Termes IGN] indice de diversité
[Termes IGN] indice de végétation
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] outil d'aide à la décision
[Termes IGN] Pakistan
[Termes IGN] pédologie locale
[Termes IGN] Pennsylvanie (Etats-Unis)
[Termes IGN] Pinus sylvestris
[Termes IGN] système d'information géographique
[Termes IGN] traitement d'imageIndex. décimale : 35.41 Applications de télédétection - végétation Résumé : (Editeur) The book contemplates different ways of approaching the study of vegetation as well as the type of indices to be used. However, all the works pursue the same objective: to know and interpret nature from different points of view, either through knowledge of nature in situ or the use of technology and mapping using satellite images. Chapters analyze the ecological parameters that affect vegetation, the species that make up plant communities, and the influence of humans on vegetation. Note de contenu : 1. Introductory Chapter: Methodological Aspects for the Study of Vegetation / Eusebio Cano Carmona, Ricardo Quinto Canas, Ana Cano Ortiz and Carmelo María Musarella
2. Using GIS and the Diversity Indices: A Combined Approach to Woody Plant Diversity in the Urban Landscape / Tuba Gül Doğan and Engin Eroğlu
3. Classical and Modern Remote Mapping Methods for Vegetation Cover / Algimantas Česnulevičius, Artūras Bautrėnas, Linas Bevainis and Donatas Ovodas
4. Assessment of the State of Forest Plant Communities of Scots Pine (Pinus sylvestris L.) in the Conditions of Urban Ecosystems / Elena Runova, Vera Savchenkova, Ekaterina Demina-Moskovskaya and Anastasia Baranenkova
5. Landscape Genetics and Phytogeography of Criollo Avocadoes Persea americana from Northeast Colombia / Clara Inés Saldamando-Benjumea, Gloria Patricia Cañas-Gutiérrez, Jorge Muñoz and Rafael Arango Isaza
6. The Use of NDVI and NDBI to Provide Subsidies to Public Manager’s Decision Making on Maintaining the Thermal Comfort in Urban Areas / Arthur Santos, Fernando Santil and Claudionor Silva
7. Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping / Maria Polivova and Anna Brook
8. Predictive Models for Reforestation and Agricultural Reclamation: A Clearfield County, Pennsylvania Case Study / Zhi Yue and Jon Bryan Burley
9. Dynamic-Catenal Phytosociology for Evaluating Vegetation / Sara del Río, Raquel Alonso-Redondo, Alejandro González-Pérez, Aitor Álvarez-Santacoloma, Giovanni Breogán Ferreiro Lera and Ángel Penas
10. Germination and Seedling Growth of Entandrophragma bussei Harms ex Engl. from Wild Populations / Samora M. Andrew, Siwa A. Kombo and Shabani A.O. Chamshama
11. Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan / Amjad ur Rahman, Esra Gürbüz, Semih Ekercin and Shujaul Mulk Khan
12. Understanding Past and Present Vegetation Dynamics Using the Palynological Approach: An Introductory Discourse / Sylvester Onoriode Obigba
13. Forest Vegetation and Dynamics Studies in India / Madan Prasad Singh, Manohara Tattekere Nanjappa, Sukumar Raman, Suresh Hebbalalu Satyanatayana, Ayyappan Narayanan, Ganesan Renagaian and Sreejith Kalpuzha Ashtamoorthy
14. Photosynthetic Antenna Size Regulation as an Essential Mechanism of Higher Plants Acclimation to Biotic and Abiotic Factors: The Role of the Chloroplast Plastoquinone Pool and Hydrogen Peroxide / Maria M. Borisova-Mubarakshina, Ilya A. Naydov, Daria V. Vetoshkina, Marina A. Kozuleva, Daria V. Vilyanen, Natalia N. Rudenko and Boris N. Ivanov
15. Rockbee Repellent Endemic Plant Species of Andaman-Nicobar Archipelago in the Bay of Bengal / Sam Paul Mathew and Raveendranpillai Prakashkumar
16. Evaluating Insects as Bioindicators of the Wetland Environment Quality (Arid Region of Algeria) / Brahimi Djamel, Rahmouni Abdelkader, Brahimi Abdelghani and Mesli LotfiNuméro de notice : 26797 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87465 Date de publication en ligne : 23/02/2022 En ligne : https://doi.org/10.5772/intechopen.87465 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100059 Adaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images / Congcong Wang in Remote sensing, vol 13 n° 24 (December-2 2021)
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Titre : Adaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images Type de document : Article/Communication Auteurs : Congcong Wang, Auteur ; Wenbin Sun, Auteur ; Deqin Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] détection de changement
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] pondération
[Termes IGN] réseau neuronal siamois
[Termes IGN] transformation en ondelettesRésumé : (auteur) The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, resulting in unsatisfactory effects of small objects detection and boundary positioning in high-resolution remote sensing images change detection. To address the problems, a network architecture based on 2-dimensional discrete wavelet transform and adaptive feature weighted fusion is proposed. The proposed network takes Siamese network and Nested U-Net as the backbone; 2-dimensional discrete wavelet transform is used to replace the pooling layer; and the inverse transform is used to replace the upsampling to realize image reconstruction, reduce the loss of spatial information, and fully retain the original image information. In this way, the proposed network can accurately detect changed objects of different scales and reconstruct change maps with clear boundaries. Furthermore, different feature fusion methods of different stages are proposed to fully integrate multi-scale and multi-level features and improve the comprehensive representation ability of features, so as to achieve a more refined change detection effect while reducing pseudo-changes. To verify the effectiveness and advancement of the proposed method, it is compared with seven state-of-the-art methods on two datasets of Lebedev and SenseTime from the three aspects of quantitative analysis, qualitative analysis, and efficiency analysis, and the effectiveness of proposed modules is validated by an ablation study. The results of quantitative analysis and efficiency analysis show that, under the premise of taking into account the operation efficiency, our method can improve the recall while ensuring the detection precision, and realize the improvement of the overall detection performance. Specifically, it shows an average improvement of 37.9% and 12.35% on recall, and 34.76% and 11.88% on F1 with the Lebedev and SenseTime datasets, respectively, compared to other methods. The qualitative analysis shows that our method has better performance on small objects detection and boundary positioning than other methods, and a more refined change map can be obtained. Numéro de notice : A2021-920 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13244971 Date de publication en ligne : 07/12/2021 En ligne : https://doi.org/10.3390/rs13244971 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99244
in Remote sensing > vol 13 n° 24 (December-2 2021) . - n°[article]Efficient occluded road extraction from high-resolution remote sensing imagery / Dejun Feng in Remote sensing, vol 13 n° 24 (December-2 2021)
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Titre : Efficient occluded road extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Dejun Feng, Auteur ; Xingyu Shen, Auteur ; Yakun Xie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4974 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de partie cachée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] reconstruction de routeRésumé : (auteur) Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas. Numéro de notice : A2021-889 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13244974 Date de publication en ligne : 07/12/2021 En ligne : https://doi.org/10.3390/rs13244974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99243
in Remote sensing > vol 13 n° 24 (December-2 2021) . - n° 4974[article]Mapping temperate forest tree species using dense Sentinel-2 time series / Jan Hemmerling in Remote sensing of environment, vol 267 (December-15 2021)
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PermalinkBuilding detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)
PermalinkComparative analysis for methods of building digital elevation models from topographic maps using geoinformation technologies / Vadim Belenok in Geodesy and cartography, vol 47 n° 4 (December 2021)
PermalinkPermalinkDiResNet: Direction-aware residual network for road extraction in VHR remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)
PermalinkFlexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
PermalinkPermalinkMSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)
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