Made available in DSpace on 2014-06-12T18:29:09Z (GMT). No. of bitstreams: 2 arquivo4274_1.pdf: 5027595 bytes, checksum: 37a31f281a0f888465edbdc60cb2db39 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Nessa tese estudamos a estimação por máxima verossimilhança (MV) do parâmetro de aspereza da distribuição G 0 A de imagens com speckle (Frery et al., 1997). Descobrimos que, satisfeita uma certa condição dos momentos amostrais, a função de verossimilhança é monótona e as estimativas MV são infinitas, implicando uma região plana. Implementamos quatro estimadores de correção de viés em uma tentativa de obter estimativas MV finitas. Três dos estimadores são obtidos da literatura sobre verossimilhança monótona (Firth, 1993; Jeffreys, 1946) e um, baseado em reamostragem, é proposto pelo autor. Fazemos experimentos numéricos de Monte Carlo para comparar os quatro estimadores e encontramos que não existe um favorito claro, a menos quando um parâmetro (dado a priori da estimação) toma um valor específico. Também aplicamos os estimadores a dados reais de radar de abertura sintética. O resultado desta análise mostra que os estimadores precisam ser comparados com base em suas habilidades de classificar regiões corretamente como ásperas, planas, ou intermediárias e não pelos seus vieses e erros quadráticos médios

Dans le cycle de carbone à l'échelle de la planète, la contribution des forêts tropicales, en tant que stock de carbone, est déterminante. Les études actuelles montrent que la connaissance précise de la biomasse forestière globale est nécessaire pour les modèles de prévision. C'est dans ce contexte que le projet BIOMASS est choisi par l'Agence spatiale européenne (ESA) comme une phase A du programme «Earth Core Mission». L'objectif de cette mission innovatrice est l'utilisation d'un système d'imagerie polarimétrique fonctionnant en bande P (435 MHz) pour la mesure de la biomasse forestière. La définition actuelle de la mission prévoit un mode tomographique rassurant une imagerie tri-dimentionnelle (3-D) de la forêt. Dans le cadre du projet BIOMASS, cette thèse de doctorat vise à développer une nouvelle stratégie pour la télédétection de la biomasse dans les forêts tropicales en utilisant des données multi-baseline acquises par le radar à ouverture synthétique (SAR) en bande P. Une approche originale consite à combiner la tomographie et le modèle RvoG (Random-Volume-over-Ground) établi et vérifié avec la technique PolInSAR (polarimetric SAR Interferometry). L'environnement forestier peut être décrit avec précision par un modèle polarimétrique multicouche (sol et succession de couches végétales). Une généralisation multi-baseline du modèle RVoG implique un certain nombre de paramètres qui peuvent être estimés à partir des données SAR en utilisant des méthodes spectrales haute résolution. Ainsi, une cartographie de la forêt et du sol peut être réalisée à l'aide de données tomographiques. De plus, la capacité des techniques tomographiques permet d'estimer la distribution verticale de la puissance rétrodiffusée. Ainsi, une information précise sur la biomasse peut être extraite de la puissance mesurée dans un domaine adapté à la couche de végétation. Cependant, cette puissance mesurée peut être fortement affectée par l'écho du sol dû à la contribution de double rebond. Et par suite, le principal défi peut être résumé par l'élaboration d'un nouvel estimateur de la biomasse forestière lié à une puissance rétrodiffusée mesurée avec une polarisation et un domaine vertical, tous les deux sont adaptés à la couche de végétation. Les algorithmes développés pour la cartographie de la forêt, l'estimation et la simulation de la biomasse sont appliqués et validés sur des données SAR aéroportées réalisées lors de la campagne TROPISAR en Guyane. / Forested areas cover one third of earth's land surface and their contribution in the storage of carbon is decisive. Current studies show that the accurate knowledge of global forest biomass is necessary for the prediction of climate changes on the planet. In this context, the BIOMASS project is selected by the European Space Agency (ESA) as Phase A of the 'Earth Core Mission' program. This highly innovative mission consists of the use of a polarimetric imaging radar operating at P band (435 MHz) for the measurement of forest biomass. The current definition of the mission provides a three-dimensional imaging (3-D) of the forest with both tomographic and multi-pass interferometric modes. In the framework of this project, this PHD thesis aims to develop a novel strategy for the remote sensing of the biomass within the dense tropical forests by processing on multi-baseline P-band Synthetic Aperture Radar (SAR) data. An original approach combines the possibilities of 3-D exploration tomography and the Random-Volume- over-Ground (RVoG) model established and verified with PolInSAR technique (Polarimetric Interferometry SAR). The forested environment can be accurately described by a polarimetric multi-layer model (soil and a succession of vegetationlayers). A multi-baseline generalization of the RVoG model involves a certain number of parameters which must be estimated from radar observation data by using High- Resolution spectral estimation tomographic methods. Thereby, a cartography of the forest and its underlying ground can be made using tomographic data. Furthermore, the capacity of the tomographic techniques on 3-D imaging allows an estimation of the vertical distribution of the backscattered power. Thus, an accurate biomass information may be extracted from the power measured at a domain adapted to the canopy layer. However, this measured backscattered may be strongly affected by the ground echo due to the double bounce contribution. The main challenge of this thesis is to establish a novel biomass estimator related to a backscattered powermeasured with a polarimetric channel and at a vertical domain, both adapted to the canopy layer. The proposed algorithms of forest cartography and biomass estimation are applied and validated on Airborne P-band SAR data realized on the TROPISAR campaign in French Guyana.

The sea surface microlayer is a millimeter-scale interfacial layer between the atmosphere and the ocean. A number of studies have suggested that there is a unique ecosystem for marine bacteria in the sea surface microlayer, but little information exists on the microbial community composition of this ecosystem due to sampling complexities. In this work, we present an improved method to sample and compare the bacterial diversity of the sea surface microlayer with that of subsurface water at the same site. Bacterial samples were collected from the sea surface microlayer with a sampling method, which minimized sample contamination from the research platform and the subsurface water. Sampling was conducted using a polycarbonate membrane filter to obtain the bacterial community structure at open water and coastal water sites in the Straits of Florida. The microlayer sampling was planned to coincide with synthetic aperture radar satellite overpasses (COSMO SkyMed), which capture a range of fine-scale features on the sea surface. The presence of surfactants affect the synthetic aperture radar imaging process because surfactants in the sea surface microlayer suppress short gravity-capillary ocean surface waves, thereby decreasing the backscatter and allowing the radar to detect surfactant-covered areas. Although sources of surfactants vary, certain marine bacteria are known to produce and transform surfactants, which suggest that these surfactant-related marine bacteria have an important biological influence on fine-scale synthetic aperture radar satellite imagery. Therefore, the comparison between synthetic aperture radar satellite images and in situ field samples may be used for interpreting and studying fine-scale features on the sea surface. The surfactant-associated bacterial composition of the sampling sites was determined using high-throughput, 454 pyrosequencing methods. A total of 61,663 sequences were analyzed and the results indicated the presence of surfactant-associated bacteria such as Moraxellaceae, Halomonadaceae, Enterobacteriaceae, Bacillaceae, and Nocardiaceae. By establishing these bacterial groups that influence the presence of surfactants, remote sensing techniques which involve monitoring the microlayer are expected to be enhanced and may provide additional information on the state of the upper ocean ecosystem.

Under low wind speed conditions, surfactants accumulate at the air-sea interface, dampen short-gravity capillary (Bragg) waves, and form natural sea slicks that are detectable visually and in synthetic aperture radar (SAR) imagery. Marine organisms, such as phytoplankton, zooplankton, seaweed, and bacteria, produce and degrade surfactants during various life processes. This study coordinates in situ sampling with TerraSAR-X satellite overpasses in order to help guide microbiological analysis of the sea surface microlayer (SML) and associated subsurface water (SSW). Samples were collected in the Gulf of Mexico during a research cruise (LASER) in February 2016 to determine abundance of surfactant associated bacteria in the sea surface microlayer and subsurface water column. By using real time polymerase chain reaction (quantitative PCR, or qPCR) to target Bacillus spp. associated with surfactant production, results indicate that more surfactant-associated bacteria reside in the subsurface water in low wind speed conditions. Sequencing results suggest that Bacillus and Pseudomonas are more abundant in the SSW in low wind speed conditions. These results indicate that these bacteria reside in the SSW, presumably producing surfactants that move to the surface via physical processes, accumulate on and enrich the sea surface microlayer.

Le suivi de l’évolution du manteau neigeux est directement lié à des enjeux socio-économiques majeurs en zone de montagne. Parmi ces enjeux figure la prévision du risque d’avalanche qui s’appuie principalement sur des observations et sur la connaissance de l’état du manteau neigeux et de son évolution dans le temps. Dans cette thèse, co-financée par le CNES et par Météo- France, nous avons évalué l’apport d’observations de télédétection spatiale active micro-ondes issues du radar à synthèse d’ouverture (SAR) de Sentinel-1, pour suivre l’évolution de certaines propriétés du manteau neigeux. Dans un premier temps, nous avons évalué la chaîne de modélisation SAFRAN-ISBA/Crocus-MEMLS par rapport aux données Sentinel-1 pré-traitées sur 3 saisons hivernales de 2014 à 2017, sur une zone de 2310 km2 à 20 m de résolution dans les Alpes du Nord françaises. Nous avons montré que les données SAR étaient pertinentes pour suivre l’évolution du manteau neigeux et, avons démontré la capacité de la chaîne de modélisation à reproduire les variations du signal observé dans le temps malgré de forts biais négatifs en cas de neige humide. Nous nous sommes intéressés à la valeur ajoutée des observations SAR de Sentinel-1 pour cartographier la neige humide, c’est-à-dire, la neige avec un taux élevé d’eau liquide. Des comparaisons ont été effectuées entre les produits neige humide obtenus par Sentinel-1 et les produits neige de Sentinel-2 distribués par Theia. Cette étude a été menée sur la saison hivernale 2017-2018, qui a connu un enneigement exceptionnel. Ces travaux ouvrent la voie à l’assimilation de données de télédétection SAR dans le modèle de neige Crocus ainsi qu’à une plus grande exploitation de ces données dans le cadre du suivi de l’enneigement pour de multiples applications. / Monitoring snowpack properties in moutainous areas is directly related to major socio-economic issues. Among these issues, avalanche prediction works through a range of tools based on meteorological and snow observations and modeling. In this thesis, co-funded by the CNES and Météo-France, we evaluated the contribution of Sentinel-1 synthetic aperture radar (SAR) remote sensing observations to study the snowpack properties and the quality of the simulations for assimilation in a snowpack model. As a first step, we evaluated the SAFRAN-ISBA/Crocus- MEMLS modeling chain against pre-processed Sentinel-1 data for 3 winter seasons from 2014 to 2017 over an area of 2310 km2 in the Northern French Alps. We have shown that SAR data are relevant for monitoring snowpack evolution and demonstrated the ability of the modeling chain to reproduce observed signal variations despite strong negative bias in wet snow conditions. We focused on wet snow products derived from Sentinel-1 SAR observations in synergy with snow absence/presence products derived from visible Sentinel-2 observations. This study was conducted on the winter season 2017-2018, which was remarkable for its snow and avalanche conditions. Such combined products make it possible to follow the spatio-temporal variability of mountain wet snow and dry snow at high elevation. This work opens the way for the assimilation of SAR remote sensing data into the Crocus snowpack model as well as greater exploitation of this data in the context of avalanche snow monitoring and prediction for a variety of purposes.

abstract: The objective of this work is to design a novel method for imaging targets and scenes which are not directly visible to the observer. The unique scattering properties of terahertz (THz) waves can turn most building surfaces into mirrors, thus allowing someone to see around corners and various occlusions. In the visible regime, most surfaces are very rough compared to the wavelength. As a result, the spatial coherency of reflected signals is lost, and the geometry of the objects where the light bounced on cannot be retrieved. Interestingly, the roughness of most surfaces is comparable to the wavelengths at lower frequencies (100 GHz – 10 THz) without significantly disturbing the wavefront of the scattered signals, behaving approximately as mirrors. Additionally, this electrically small roughness is beneficial because it can be used by the THz imaging system to locate the pose (location and orientation) of the mirror surfaces, thus enabling the reconstruction of both line-of-sight (LoS) and non-line-of-sight (NLoS) objects. Back-propagation imaging methods are modified to reconstruct the image of the 2-D scenario (range, cross-range). The reflected signal from the target is collected using a SAR (Synthetic Aperture Radar) set-up in a lab environment. This imaging technique is verified using both full-wave 3-D numerical analysis models and lab experiments. The novel imaging approach of non-line-of-sight-imaging could enable novel applications in rescue and surveillance missions, highly accurate localization methods, and improve channel estimation in mmWave and sub-mmWave wireless communication systems. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

In the long term it is beneficial to a country's economy to exploit the maritime environment surrounding it responsibly. It is also beneficial to protect this environment from poaching and pollution. To achieve this the responsible parties of a country must have an awareness of what is transpiring in the maritime domain. Synthetic aperture radar can provide an image, regardless of weather or light conditions, of the ocean showing most vessels therein. To monitor the ocean, using synthetic aperture radar imagery, at the lowest cost would require large swath synthetic aperture radar imagery. There exists a trade-off between large swath imagery and the image's resolution resulting in the largest swath image having the poorest resolution. Existing research has shown that it is possible to use coarse resolution synthetic aperture radar imagery to detect vessels at sea, but little work has been done on classifying those vessels. This research aims to investigate the coarse resolution classification information gap. This is done by using a dataset of matching synthetic aperture radar and ship transponder data to train a statistical classification algorithm in order to classify or estimate the length of vessels based on features extracted from their synthetic aperture radar image. The results of this research show that coarse resolution (approximately 40 m per pixel) synthetic aperture radar imagery is able to estimate vessel size for larger classes and provides insight on which vessel classes would require finer resolutions in order to be detected and classified reliably. The range of smaller vessel classes is usually limited to ports and fishing zones. These zones can be mapped using historical vessel transponder data and so a dedicated surveillance campaign can be optimised to use higher resolution products in these areas. The size estimation from the machine learning algorithm performs better than current techniques. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

No description available.

Cette thèse présente la méthode de détection de changements MIMOSA (Method for generalIzed Means Ordered Series Analysis). Cette nouvelle méthode permet de détecter automatiquement des changements entre couples ou séries temporelles d’images SAR. En effet, grâce aux moyennes temporelles, le nombre d’images en jeu n’importe plus puisque seulement deux moyennes différentes sont comparées de sorte à détecter les changements (par exemple moyenne géométrique et moyenne quadratique). De ce fait, les grand volumes de données disponibles de nos jours sont exploitables plus facilement puisque l’information utile est «résumée» dans les moyennes. Le seul paramètre de la détection est le taux de fausses alarmes obtenu dans le résultat, ce qui rend son analyse plus intuitive. Les cartes de changements fournies par MIMOSA sont de très bonne qualité en comparaison à celles fournies par d’autres méthodes. De nombreux tests ont été mis en place pour constater la robustesse de la méthode MIMOSA face aux problèmes les plus souvent rencontrés, comme une mauvaise calibration radiométrique, ou encore un mauvais recalage. Une interface graphique a de plus été développée autour de MIMOSA, incorporant de nombreux outils de préparation et traitement des données, ainsi que des outils d’analyse des résultats. / This PhD thesis presents the MIMOSA (Method for generalIzed Means Ordered Series Analysis) change detection methood. This new technique can automatically detect changes between SAR image pairs or within time series. Indeed, thanks to the temporeal means, the number of involved images doesn’t matters because only two different means are compared to detect the changes (for example, the geometric and quadratic means). Thus, large data volumes can be processed easily, since the useful information is condensed within the temporal means. The only change detection parameter is the false alarm rate that will be MIMOSA method are very good compared to other methods. Several tests have been performed in order to quantify the robustness of the method facing the most common problems, like image misregistration or radiometric calibration errors. A graphical user interface has also been developed for MIMOSA, including many useful tools to prepare and process SAR data, but also several analyse tools.

No description available.