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Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China / Ziyi Cao in Open geosciences, vol 14 n° 1 (January 2023)  

Public [article]  inOpen geosciences > vol 14 n° 1 (January 2023) . - pp 89 - 97 Titre : Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China Type de document : Article/Communication Auteurs : Ziyi Cao, Auteur ; Heng Zhang, Auteur ; Yan Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 89 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse diachronique [Termes IGN] autocorrélation spatiale [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] séisme [Termes IGN] sismicité Résumé : (auteur) Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P Numéro de notice : A2023-052 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article En ligne : https://doi.org/10.1515/geo-2020-0332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102383 [article]

Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method / Hongliang Lu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 99 - 118 Titre : Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method Type de document : Article/Communication Auteurs : Hongliang Lu, Auteur ; Heng Zhang, Auteur ; Huaitao Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 99 - 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande P [Termes IGN] Chine [Termes IGN] compensation [Termes IGN] erreur de mesure [Termes IGN] erreur de phase [Termes IGN] Guyane (département français) [Termes IGN] hauteur des arbres [Termes IGN] image radar moirée [Termes IGN] ligne de base [Termes IGN] polarisation [Termes IGN] tomographie radar [Termes IGN] triangulation de Delaunay Résumé : (auteur) Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency’s campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation. Numéro de notice : A2021-271 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.022 Date de publication en ligne : 14/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.022 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97329 [article]

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081-2021051	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2021053	DEP-RECP	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Multispectral object detection / Heng Zhang (2021)  

Public Titre : Multispectral object detection Type de document : Thèse/HDR Auteurs : Heng Zhang, Auteur ; Elisa Fromont, Directeur de thèse ; Sébastien Lefèvre, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2021 Importance : 114 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse présentée en vue de l’obtention du grade de docteur en Informatique de l'Université de Rennes 1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] chambre de prise de vue thermique [Termes IGN] détection d'objet [Termes IGN] données d'entrainement sans étiquette [Termes IGN] efficacité [Termes IGN] fusion de données multisource [Termes IGN] image multibande [Termes IGN] précision de la classification [Termes IGN] qualité du modèle [Termes IGN] segmentation sémantique Index. décimale : THESE Thèses et HDR Résumé : (Auteur) Only using RGB cameras for automatic outdoor scene analysis is challenging when, for example, facing insufficient illumination or adverse weather. To improve the recognition reliability, multispectral systems add additional cameras (e.g. infra-red) and perform object detection from multispectral data. Although multispectral scene analysis with deep learning has been shown to have a great potential, there are still many open research questions and it has not been widely deployed in industrial contexts. In this thesis, we investigated three main challenges about multispectral object detection: (1) the fast and accurate detection of objects of interest from images; (2) the dynamic and adaptive fusion of information from different modalities;(3) low-cost and low-energy multispectral object detection and the reduction of its manual annotation efforts. In terms of the first challenge, we first optimize the label assignment of the object detection training with a mutual guidance strategy between the classification and localization tasks; we then realize an efficient compression of object detection models including the teacher-student prediction disagreements in a feature-based knowledge distillation framework. With regard to the second challenge, three different multispectral feature fusion schemes are proposed to deal with the most difficult fusion cases where different cameras provide contradictory information. For the third challenge, a novel modality distillation framework is firstly presented to tackle the hardware and software constraints of current multispectral systems; then a multi-sensor-based active learning strategy is designed to reduce the labeling costs when constructing multispectral datasets. Note de contenu : 1. Introduction 1.1 Context and motivations 1.2 Thesis outline 2. Deep learning background 2.1 General object detection 2.2 Multispectral object detection 2.3 Knowledge distillation 2.4 Active learning 2.5 Datasets 3. Efficient object detection on embedded devices 3.1 Best practices for training object detection models 3.2 Mutual Guidance for Anchor Matching 3.3 Prediction Disagreement aware Feature Distillation 3.4 Experimental results 4. Information fusion from multispectral data 4.1 Multispectral Fusion with Cyclic Fuse-and-Refine 4.2 Progressive Spectral Fusion 4.3 Experimental results for CFR and PS-Fuse 4.4 Guided Attentive Feature Fusion 4.5 Experimental results for GAFF 5. Sensors and annotations: low cost multispectral data processing 5.1 Deep Active Learning from Multispectral Data 5.2 Low-cost Multispectral Scene Analysis with Modality Distillation 6. Conclusions and future works 6.1 Conclusions 6.2 Application to remote sensing data 6.3 Perspectives Numéro de notice : 26765 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Rennes 1 : 2021 Organisme de stage : (IRISA) INRIA nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/01/2022 En ligne : https://hal.science/tel-03530257/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99855