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Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm / Maeve Blarel (2022)
Titre : Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm Type de document : Mémoire Auteurs : Maeve Blarel, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 73 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aérosol
[Termes IGN] conversion de données
[Termes IGN] correction atmosphérique
[Termes IGN] correction géométrique
[Termes IGN] correction radiométrique
[Termes IGN] image hyperspectrale
[Termes IGN] image Venµs-VSSC
[Termes IGN] Israël
[Termes IGN] microsatellite
[Termes IGN] Python (langage de programmation)
[Termes IGN] série temporelle
[Termes IGN] traitement de données localiséesIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) Réalisé au sein du laboratoire de télédétection de l’Institut Jacob Blaustein pour la recherche sur le désert (BIDR) de l’Université Ben-Gourion du Négev, en Israël, et financé par une mission du CNRS, ce stage a pour objectif l’adaptation d’un driver dédié à la conversion des données satellites VENµS et à leur préparation pour le traitement par l’algorithme GRASP. VENµS (Vegetation and Environment monitoring New Micro-Satellite) est un microsatellite, fruit d’une collaboration franco-israélienne pour l’observation de la Terre à l’aide d’une caméra super spectrale. Les visées de la mission scientifique sont déterminées par le CESBIO et le CNES, en France, et l’Université Ben-Gourion du Néguev, en Israël. Son objectif est de fournir des observations à haute résolution spatiale pour la recherche scientifique portant sur la surveillance, l’analyse et la caractérisation du fonctionnement de la surface terrestre, sous les effets de facteurs environnementaux et des activités humaines. Plus particulièrement, ces données sont dédiées à des applications dans l’agriculture de précision, l’urbanisation et la surveillance des masses d’eau. Les images acquises au-dessus d’Israël ont un format différent de celles prises à travers le monde pour une gestion distincte des données. Aujourd’hui, les recherches israélienne et française souhaitent une caractérisation des aérosols atmosphériques sur Israël et un traitement des données par GRASP. La problématique rencontrée est la conversion des données sur Israël pour leur entrée dans cet algorithme. Après une phase de découverte et de compréhension des données satellites VENµS et celles requises à l’entrée de GRASP, le travail de ce présent stage consiste à développer une solution d’adaptation du programme informatique pour la conversion des données VENµS sur Israël. Des perspectives existent pour ce projet. Pour observer la Terre, on souhaite des données de plus en plus précises par des améliorations de l’acquisition et du traitement des images. Concernant l’acquisition de données, les intervalles de temps de revisite limitent actuellement l’avantage multi-pixel. D’un autre côté, l’un des objectifs de cette mission satellitaire est le développement des algorithmes pour exploiter des séries temporelles de données, incluant les corrections géométriques et radiométriques. Pour GRASP, la gestion du masque des nuages doit être perfectionnée et concernant le driver adapté, les observations directionnelles demandent une exploitation plus grande. L’ensemble des codes Python, fonctionnels et commentés, implémenté au cours du stage est confidentiel et reste à la propriété de GRASP. Par conséquent, aucun script provenant du code source ne sera présenté au cours de ce rapport. Note de contenu : Introduction
1. Internship presentation
1.1 Context
1.2 Issues and Objectives
1.3 State of current research
2. Technical study
2.1 Driver architecture
2.2 Language, libraries and software in use
2.3 The data
3. Achievement
3.1 Implementation
3.2 Progress of internship
3.3 Difficulties encountered and Solutions adopted
ConclusionNuméro de notice : 26872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : Laboratoire de télédétection de l’Institut Jacob Blaustein (Université Ben-Gourion du Négev) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101702 Documents numériques
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Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm - pdf auteurAdobe Acrobat PDF Self-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (2022)
[article]
Titre : Self-attention and generative adversarial networks for algae monitoring Type de document : Article/Communication Auteurs : Nhut Hai Huynh, Auteur ; Gordon Boër, Auteur ; Hauke Schramm, Auteur Année de publication : 2022 Article en page(s) : pp 10 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algue
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image hyperspectrale
[Termes IGN] plancton
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Water is important for the natural environment and human health. Monitoring algae concentrations yield information on the water quality. Compared with in situ measurements of water quality parameters, which are often complex and expensive, remote sensing techniques, using hyperspectral data analysis, are fast and cost-effective. The objectives of this study are (1) to estimate the algae concentrations from hyperspectral data using deep learning techniques, (2) to investigate the applicability of attention mechanisms in the analysis of hyperspectral data, and (3) to augment the training data using generative adversarial networks (GANs). The results show that the accuracy of deep learning techniques is 7.6% higher than that of simpler artificial neural networks. Compared to noise injection and principal component analysis-based data augmentation, the use of a GAN-based data augmentation method significantly improves the accuracy of algae concentration estimates (>5%). In addition, models with added attention mechanisms yield an on average 3.13% higher accuracy than those without attention techniques. This result demonstrates the improvement of spectral features of artificial hyperspectral data based on the self-attention approach, revealing the potential of attention techniques in hyperspectral remote sensing. Numéro de notice : A2022-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2021.2010605 Date de publication en ligne : 02/01/2022 En ligne : https://doi.org/10.1080/22797254.2021.2010605 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99547
in European journal of remote sensing > vol 55 n° 1 (2022) . - pp 10 - 22[article]Early 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)
[article]
Titre : Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany Type de document : Article/Communication Auteurs : Kathrin Einzmann, Auteur ; Clement Atzberger, Auteur ; Nicole Pinnel, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bavière (Allemagne)
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dépérissement
[Termes IGN] détection de changement
[Termes IGN] houppier
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] insecte nuisible
[Termes IGN] phénomène climatique extrême
[Termes IGN] Picea abies
[Termes IGN] réflectance spectrale
[Termes IGN] série temporelle
[Termes IGN] stress hydriqueRésumé : (auteur) Vitality loss of trees caused by extreme weather conditions, drought stress or insect infestations, are expected to increase with ongoing climate change. The detection of vitality loss at an early stage is thus of vital importance for forestry and forest management to minimize ecological and economical damage. Remote sensing instruments are able to detect changes over large areas down to the level of individual trees. The scope of our study is to investigate whether it is possible to detect stress-related spectral changes at an early stage using hyperspectral sensors. For this purpose, two Norway spruce (Picea abies) forest stands, both different in age and maintenance, were monitored in the field over two vegetation periods. In parallel, time series of airborne hyperspectral remote sensing data were acquired. For each stand 70 trees were artificially stressed (ring-barked) and 70 trees were used as control trees. The data collected in south-eastern Germany consists of measurements at multiple times and at different scales: (1) crown conditions were visually assessed in the field (2) needle reflectance spectra were acquired in the laboratory using a FieldSpec spectrometer, and (3) hyperspectral airborne data (HySpex) were flown at 0.5 m spatial resolution. We aimed for a simultaneous data acquisition at the three levels. This unique data set was investigated whether any feature can be discriminated to detect vitality loss in trees at an early stage. Several spectral transformations were applied to the needle and tree crown spectra, such as spectral derivatives, vegetation indices and angle indices. All features were examined for their separability (ring-barked vs. control trees) with the Random Forest (RF) classification algorithm. As result, the younger, well maintained forest stand only showed minor changes over the 2-year period, whereas changes in the older forest stand were observable both in the needle and in the hyperspectral tree crown spectra, respectively. These changes could even be detected before changes were visible by field observations. The tree spectral reactions to ring-barking were first noticeable 11 months after ring-barking and 6 weeks before they were visible by field inspection. The most discriminative features for separating the two groups were the reflectance spectra and the spectral derivatives, over the VIs or angle indices. The tree crown spectra of the two groups could be separated by the RF classifier with a 79% overall accuracy at the beginning of the second vegetation period and 1 month later with 92% overall accuracy with high kappa index. The results clearly demonstrate the great potential of hyperspectral remote sensing in detecting early vitality changes of stressed trees. Numéro de notice : A2021-921 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112676 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112676 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99274
in Remote sensing of environment > vol 266 (December 2021) . - n° 112676[article]Flexible 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)
[article]
Titre : Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Qingqing Zhao, Auteur ; Jiayue Zhuang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 10394 - 10409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] ondelette de Gabor
[Termes IGN] segmentation d'image
[Termes IGN] superpixelRésumé : (auteur) Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG- Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG- Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG- Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. Numéro de notice : A2021-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3048994 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3048994 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99131
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10394 - 10409[article]Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)
[article]
Titre : Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation Type de document : Article/Communication Auteurs : Hamid Jafarzadeh, Auteur ; Masoud Mahdianpari, Auteur ; Eric Gill, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] boosting adapté
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] ensachage
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image ROSISRésumé : (auteur) In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data. Numéro de notice : A2021-823 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214405 Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.3390/rs13214405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98938
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4405[article]Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkEndmember bundle extraction based on multiobjective optimization / Rong Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkHyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkLarge-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkTarget-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkMultiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkUnsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAnti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([01/04/2021])PermalinkAutomatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkApplication of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkDetection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction / Xiaorui Song in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkImproving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])PermalinkCorrentropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)Permalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)Permalink