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scientifique

Geographic Information Systems in geospatial intelligence / Rustam B. Rustamov (2020)  

Public Titre : Geographic Information Systems in geospatial intelligence Type de document : Monographie Auteurs : Rustam B. Rustamov, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 190 p. ISBN/ISSN/EAN : 978-1-83880-505-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] Airborne Data Acquisition and Registration [Termes IGN] apprentissage automatique [Termes IGN] base de données localisées [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] Global Positioning System [Termes IGN] image hyperspectrale [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] route [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol Résumé : (Editeur) Earth observation systems, by use of space science and technology advances, present a large-scale opportunity for applying remote sensing methods with geographical information system (GIS) developments. Integrating these two methods makes it possible to achieve high-accuracy satellite data processing. This book considers aspects of GIS technology applications with space science technology and innovation approaches. It examines the potential of Earth observation satellite systems as well as existing challenges and problems in the field. Chapters cover topics such as RGB-D sensors for autonomous pothole detection, machine learning in GIS, interferometric synthetic aperture radar (InSAR) modeling, and others. Note de contenu : Chapter 1 - InSAR modeling of geophysics measurements Chapter 2 - Expanding navigation systems by integrating it with advanced technologies Chapter 3 - A review of the machine learning in GIS for megacities application Chapter 4 - Study of equatorial plasma bubbles using ASI and GPS systems Chapter 5 - Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering Chapter 6 - Clustering techniques for land use land cover classification of remotely sensed images Chapter 7 - Building an integrated database of road design elements Chapter 8 - On the use of low-cost RGB-D sensors for autonomous pothole detection with spatial fuzzy c-means segmentation Numéro de notice : 26559 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.84925 En ligne : http://doi.org/10.5772/intechopen.84925 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98242

Individual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle / Takeshi Hoshikawa in Journal of The Remote Sensing Society of Japan, vol 40 n° 1 (2020)  

Public [article]  inJournal of The Remote Sensing Society of Japan > vol 40 n° 1 (2020) . - pp 13 - 19 Titre : Individual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle Type de document : Article/Communication Auteurs : Takeshi Hoshikawa, Auteur ; Kazukiyo Yamamoto, Auteur Année de publication : 2020 Article en page(s) : pp 13 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] détection d'arbres [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] indice de végétation [Termes IGN] maladie phytosanitaire [Termes IGN] modèle de régression [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Pinus (genre) [Termes IGN] protection des forêts [Termes IGN] régression logistique [Termes IGN] semis de points Résumé : (auteur) Pine wilt disease is one of the most destructive disease of pine forests. It is important to detect and exterminate infected trees for preservation of the forest. We demonstrated a novel method combining individual tree detection (ITD) and classification by logistic regression using unmanned aerial vehicle (UAV) images for the mapping of infected trees. In the ITD phase, 50 % and 84 % of damaged trees were automatically detected from the 3D point cloud generated from the UAV images using the local maximum filter. These rates of detection were comparable to previous studies that used UAV imagery. Subsequently, five vegetation indices calculated from multispectral and visible color (RGB) images were used. Among the vegetation indices, normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and vegetation atmospherically resistant index (VARI) were preferable explanatory variable in the logistic regression to divide damaged and undamaged trees. The accuracy of these models ranged from 98 % to 100 % and the F-measure ranged from 94 % to 100 %. The best model, the logistic regression model using VARI as the explanatory variable, was then tested using five datasets to evaluate general performance. Each model showed explicitly high accuracy ranging from 95 % to 100 %. The best accuracy when considering the ITD and classification was 84 %. To map pine wilt disease, the proposed method is suitable for practical use due to its high-efficient and low-cost. Numéro de notice : A2020-405 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.11440/rssj.40.13 Date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.11440/rssj.40.13 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96090 [article]

Intelligent processing on image and optical information / Seakwon Yeom (2020)  

Public Titre : Intelligent processing on image and optical information Type de document : Monographie Auteurs : Seakwon Yeom, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 324 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-945-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de lignes [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] détection de changement [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] navigation autonome [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image Résumé : (éditeur) This book focuses on the intelligent processing of images and optical information acquired by various imaging methods. Intelligent image and optical information processing have paved the way for the recent epoch of new intelligence and information era. Certainly, information acquired by various imaging techniques is of tremendous value; thus, an intelligent analysis of them is necessary to make the best use of it. A broad range of research fields is included in this book. Many studies focus on object classification and detection. Registration, segmentation, and fusion are performed between a series of images. Many valuable and up-to-most recent technologies are provided to solve the real problems in selected papers. Note de contenu : 1- Special issue on intelligent processing on image and optical information 2- Change detection of water resources via remote sensing: An L-V-NSCT approach 3- A texture classification approach based on the integrated optimization for parameters and features of gabor filter via hybrid ant lion optimizer 4- Real-time automated segmentation and classification of calcaneal fractures in CT images 5- Automatic zebrafish egg phenotype recognition from bright-field microscopic images using deep convolutional neural network 6- Zebrafish larvae phenotype classification from bright-field microscopic images using a two-tier deep-learning pipeline 7- Unsupervised generation and synthesis of facial images via an auto-encoder-based deep generative adversarial network 8- Detecting green mold pathogens on lemons using hyperspectral images 9- Review on computer aided weld defect detection from radiography images 10- Feature extraction with discrete non-separable shearlet transform and its application to surface inspection of continuous casting slabs 11- A novel extraction method for wildlife monitoring images with wireless multimedia sensor networks (WMSNs) 12- IMU-aided high-frequency Lidar odometry for autonomous driving 13- Determination of the optimal state of dough fermentation in bread production by using optical sensors and deep learning 14- Multi-sensor face registration based on global and local structures 15- Multifocus image fusion using a sparse and low-rank matrix decomposition for aviator’s night vision Goggle 16- Error resilience for block compressed sensing with multiple-channel transmission 17- Image completion with hybrid interpolation in tensor representation 18- A correction method for heat wave distortion in digital image correlation measurements based on background-oriented schlieren 19- An effective optimization method for machine learning based on ADAM 20- Boundary matching and interior connectivity-based cluster validity analysis Numéro de notice : 28438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-945-4 En ligne : https://doi.org/10.3390/books978-3-03936-945-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98875

Processing and analysis of hyperspectral data / Jie Chen (2020)  

Public Titre : Processing and analysis of hyperspectral data Type de document : Monographie Auteurs : Jie Chen, Éditeur scientifique ; Yingying Song, Éditeur scientifique ; Hengchao Li, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 140 p. ISBN/ISSN/EAN : 978-1-78985-109-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] image à haute résolution [Termes IGN] image hyperspectrale [Termes IGN] image proche infrarouge [Termes IGN] qualité des eaux [Termes IGN] signature spectrale [Termes IGN] turbidité des eaux Résumé : (Editeur) Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. Note de contenu : Section One - Theoretical advances of hyperspectral image processing Chapter 1 - Hyperspectral endmember extraction techniques Chapter 2 - Hyperspectral image classification Chapter 3 - Hyperspectral image super-resolution using optimization and DCNN-based methods Chapter 4 - Fast chaotic encryption for hyperspectral images Section Two - Applications of hyperspectral image processing Chapter 5 - NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration Chapter 6 - Use of hyperspectral remote sensing to estimate water quality Numéro de notice : 26560 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78179 En ligne : http://doi.org/10.5772/intechopen.78179 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98243

Recent advances in image restoration with applications to real world problems / Chiman Kwan (2020)  

Public Titre : Recent advances in image restoration with applications to real world problems Type de document : Monographie Auteurs : Chiman Kwan, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 ISBN/ISSN/EAN : 978-1-83968-356-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage non-dirigé [Termes IGN] données spatiotemporelles [Termes IGN] extraction de modèle [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] modèle numérique de terrain [Termes IGN] reconstruction 3D [Termes IGN] restauration d'image Résumé : (Editeur) In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included. Note de contenu : 1. Introductory Chapter: Recent Advances in Image Restoration 2. Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data 3. Application of Deep Learning Approaches for Enhancing Mastcam Images 4. Generative Adversarial Networks for Visible to Infrared Video Conversion 5. Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution 6. Spatiotemporal Fusion in Remote Sensing 7. 3D Reconstruction through Fusion of Cross-View Images 8. Practical Digital Terrain Model Extraction Using Image Inpainting Techniques Numéro de notice : 26695 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.90607 Date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.5772/intechopen.90607 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99081

Remote Sensing Applications for Agriculture and Crop Modelling / Piero Toscano (2020)  

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Remote sensing for target object detection and identification / Gemine Vivone (2020)  

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Remote sensing of plant biodiversity / Jeannine Cavender-Bares (2020)  

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Remote sensing technology applications in forestry and REDD+ / Kim Calders (2020)  

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Remotely sensing the species of individual trees / Yifang Shi (2020)  

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Satellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)    

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Uso de QGIS en la teledetección, Vol. 4. QGIS y sus aplicaciones en agua y en gestion del riego / Nicolas Baghdadi (2020)

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Very high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)  

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An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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