Towards optical intensity interferometry for high angular resolution stellar astrophysics

Most neighboring stars are still detected as point sources and are beyond the angular resolution reach of current observatories. Methods to improve our understanding of stars at high angular resolution are investigated. Air Cherenkov telescopes (ACTs), primarily used for Gamma-ray astronomy, enable us to increase our understanding of the circumstellar environment of a particular system. When used as optical intensity interferometers, future ACT arrays will allow us to detect stars as extended objects and image their surfaces at high angular resolution. Optical stellar intensity interferometry (SII) with ACT arrays, composed of nearly 100 telescopes, will provide means to measure fundamental stellar parameters and also open the possibility of model-independent imaging. A data analysis algorithm is developed and permits the reconstruction of high angular resolution images from simulated SII data. The capabilities and limitations of future ACT arrays used for high angular resolution imaging are investigated via Monte-Carlo simulations. Simple stellar objects as well as stellar surfaces with localized hot or cool regions can be accurately imaged. Finally, experimental efforts to measure intensity correlations are expounded. The functionality of analog and digital correlators is demonstrated. Intensity correlations have been measured for a simulated star emitting pseudo-thermal light, resulting in angular diameter measurements. The StarBase observatory, consisting of a pair of 3 m telescopes separated by 23 m, is described.Comment: PhD dissertatio

Insights into Titan’s geology and hydrology based on enhanced image processing of Cassini RADAR data

The Cassini Synthetic Aperture Radar has been acquiring images of Titan's surface since October 2004. To date, 59% of Titan's surface has been imaged by radar, with significant regions imaged more than once. Radar data suffer from speckle noise hindering interpretation of small-scale features and comparison of reimaged regions for change detection. We present here a new image analysis technique that combines a denoising algorithm with mapping and quantitative measurements that greatly enhance the utility of the data and offers previously unattainable insights. After validating the technique, we demonstrate the potential improvement in understanding of surface processes on Titan and defining global mapping units, focusing on specific landforms including lakes, dunes, mountains, and fluvial features. Lake shorelines are delineated with greater accuracy. Previously unrecognized dissection by fluvial channels emerges beneath shallow methane cover. Dune wavelengths and interdune extents are more precisely measured. A significant refinement in producing digital elevation models is shown. Interactions of fluvial and aeolian processes with topographic relief is more precisely observed and understood than previously. Benches in bathymetry are observed in northern sea Ligeia Mare. Submerged valleys show similar depth suggesting that they are equilibrated with marine benches. These new observations suggest a liquid level increase in the northern sea, which may be due to changes on seasonal or longer timescales

Application of Video Image Correlation Techniques to the Space Shuttle External Tank Foam Materials

Results that illustrate the use of a video-image-correlation-based displacement and strain measurement system to assess the effects of material nonuniformities on the behavior of the sprayed-on foam insulation (SOFI) used for the thermal protection system on the Space Shuttle External Tank are presented. Standard structural verification specimens for the SOFI material with and without cracks and subjected to mechanical or thermal loading conditions were tested. Measured full-field displacements and strains are presented for selected loading conditions to illustrate the behavior of the foam and the viability of the measurement technology. The results indicate that significant strain localization can occur in the foam because of material nonuniformities. In particular, elongated cells in the foam can interact with other geometric or material discontinuities in the foam and develop large-magnitude localized strain concentrations that likely initiate failures. Furthermore, some of the results suggest that continuum mechanics and linear elastic fracture mechanics might not adequately represent the physical behavior of the foam, and failure predictions based on homogeneous linear material models are likely to be inadequate

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

A Sensitivity Study of L-Band Synthetic Aperture Radar Measurements to the Internal Variations and Evolving Nature of Oil Slicks

This thesis focuses on the use of multi-polarization synthetic aperture radar (SAR) for characterization of marine oil spills. In particular, the potential of detecting internal zones within oil slicks in SAR scenes are investigated by a direct within-slick segmentation scheme, along with a sensitivity study of SAR measurements to the evolving nature of oil slicks. A simple, k-means clustering algorithm, along with a Gaussian Mixture Model are separately applied, giving rise to a comparative study of the internal class structures obtained by both strategies. As no optical imagery is available for verification, the within-slick segmentations are evaluated with respect to the behavior of a set of selected polarimetric features, the prevailing wind conditions and weathering processes. In addition, a fake zone detection scheme is established to help determine if the class structures obtained potentially reflect actual internal variations within the slicks. Further, the evolving nature of oil slicks is studied based on the temporal development of a set of selected geometric region descriptors. Two data sets are available for the investigation presented in this thesis, both captured by a full-polarization L-band airborne SAR system with high spatial- and temporal resolution. The results obtained with respect to the zone detection scheme developed supports the hypothesis of the existence of detectable zones within oil spills in SAR scenes. Additionally, the method established for studying the evolving nature of oil slicks is found convenient for accessing the general behavior of the slicks, and simplifies interpretation

Détection de bateaux dans les images de radar à ouverture synthétique

Le but principal de cette thèse est de développer des algorithmes efficaces et de concevoir un système pour la détection de bateaux dans les images Radar à Ouverture Synthetique (ROS.) Dans notre cas, la détection de bateaux implique en premier lieu la détection de cibles de points dans les images ROS. Ensuite, la détection d'un bateau proprement dit dépend des propriétés physiques du bateau lui-même, tel que sa taille, sa forme, sa structure, son orientation relative a la direction de regard du radar et les conditions générales de l'état de la mer. Notre stratégie est de détecter toutes les cibles de bateaux possibles dans les images de ROS, et ensuite de chercher autour de chaque candidat des évidences telle que les sillons. Les objectifs de notre recherche sont (1) d'améliorer 1'estimation des paramètres dans Ie modèle de distribution-K et de déterminer les conditions dans lesquelles un modèle alternatif (Ie Gamma, par exemple) devrait être utilise plutôt; (2) d'explorer Ie modèle PNN (Probabilistic Neural Network) comme une alternative aux modèles paramétriques actuellement utilises; (3) de concevoir un modèle de regroupement flou (FC : Fuzzy Clustering) capable de détecter les petites et grandes cibles de bateaux dans les images a un seul canal ou les images a multi-canaux; (4) de combiner la détection de sillons avec la détection de cibles de bateaux; (5) de concevoir un modèle de détection qui peut être utilisé aussi pour la détection des cibles de bateaux en zones costières.Abstract: The main purpose of this thesis is to develop efficient algorithms and design a system for ship detection from Synthetic Aperture Radar (SAR) imagery. Ship detection usually involves through detection of point targets on a radar clutter background.The detection of a ship depends on the physical properties of the ship itself, such as size, shape, and structure; its orientation relative to the radar look-direction; and the general condition of the sea state. Our strategy is to detect all possible ship targets in SAR images, and then search around each candidate for the wake as further evidence.The objectives of our research are (1) to improve estimation of the parameters in the K-distribution model and to determine the conditions in which an alternative model (Gamma, for example) should be used instead; (2) to explore a PNN (Probabilistic Neural Networks) model as an alternative to the commonly used parameteric models; (3) to design a FC (Fuzzy Clustering) model capable of detecting both small and large ship targets from single-channel images or multi-channel images; (4) to combine wake detection with ship target detection; (5) to design a detection model that can also be used to detect ship targets in coastal areas. We have developed algorithms for each of these objectives and integrated them into a system comprising six models.The system has been tested on a number of SAR images (SEASAT, ERS and RADARSAT-1, for example) and its performance has been assessed

Locating the LCROSS Impact Craters

The Lunar CRater Observations and Sensing Satellite (LCROSS) mission impacted a spent Centaur rocket stage into a permanently shadowed region near the lunar south pole. The Sheperding Spacecraft (SSC) separated \sim9 hours before impact and performed a small braking maneuver in order to observe the Centaur impact plume, looking for evidence of water and other volatiles, before impacting itself. This paper describes the registration of imagery of the LCROSS impact region from the mid- and near-infrared cameras onboard the SSC, as well as from the Goldstone radar. We compare the Centaur impact features, positively identified in the first two, and with a consistent feature in the third, which are interpreted as a 20 m diameter crater surrounded by a 160 m diameter ejecta region. The images are registered to Lunar Reconnaisance Orbiter (LRO) topographical data which allows determination of the impact location. This location is compared with the impact location derived from ground-based tracking and propagation of the spacecraft's trajectory and with locations derived from two hybrid imagery/trajectory methods. The four methods give a weighted average Centaur impact location of -84.6796\circ, -48.7093\circ, with a 1{\sigma} un- certainty of 115 m along latitude, and 44 m along longitude, just 146 m from the target impact site. Meanwhile, the trajectory-derived SSC impact location is -84.719\circ, -49.61\circ, with a 1{\sigma} uncertainty of 3 m along the Earth vector and 75 m orthogonal to that, 766 m from the target location and 2.803 km south-west of the Centaur impact. We also detail the Centaur impact angle and SSC instrument pointing errors. Six high-level LCROSS mission requirements are shown to be met by wide margins. We hope that these results facilitate further analyses of the LCROSS experiment data and follow-up observations of the impact region.Comment: Accepted for publication in Space Science Review. 24 pages, 9 figure

Statistical Fusion of Multi-aspect Synthetic Aperture Radar Data for Automatic Road Extraction

In this dissertation, a new statistical fusion for automatic road extraction from SAR images taken from different looking angles (i.e. multi-aspect SAR data) was presented. The main input to the fusion is extracted line features. The fusion is carried out on decision-level and is based on Bayesian network theory