Thesis (M.S. in Physical Oceanography) Naval Postgraduate School, December 2001. / "December 2001". Thesis advisor(s): Durkee, Philip A . ; Paduan, Jeffrey D. Includes bibliographical references (p.53-54). Also available online.

A Hyperspectral imager aboard a cubesat would be able to provide images which could be used to identify ships and determine the ship's length and breadth and heading. Depending on the size of the ship, the speed the ship is traveling can be determined as well; however the speed and size determination is limited by the spatial resolution of 100 meters. The spectral signature of the boat is dramatically different from the spectral signature of the open Ocean especially within the range of 400 to 1000 nanometers, and this threshold is the basis for extracting ship data. Hyperspectral Imagers are ideal for minimization with few optical errors introduced, and designs range in durability making them useful on board small satellites especially in the visible and near infrared region. Placing an imager on a satellite allows for consistent observation over a region to identify patterns in ship movement over time. / Master of Science

In this work we review the problem of radar imaging satellites' dependency on ground stations to transfer the image data. Since synthetic aperture radar images are very big, only ground stations are equipped to transfer that much data. This is a problem for maritime surveillance as it creates delay between the imaging and processing. We propose a new hardware algorithm that can be used by a satellite to detect ships and extract information about them, and since this information is smaller it can be relayed to reduce the delay significantly. For ship detection, an adaptive thresholding algorithm with exponential model is used. This algorithm was selected as it is the best fit for single-look radar images. For the property calculation, a data accumulating, single-look, connected component labeling algorithm is proposed. This algorithm accumulates data about the connected components, which is then used to calculate the properties of ships using image moments. The combined algorithm was then validated on Radarsat-2 images using Matlab for software and co-simulation for hardware. / Graduate

This work presents a new technique for automatic detection of marine vehicles in images and video of open sea. Users of such system include border guards, military, port safety, flow management, and sanctuary protection personnel. The source of images and video is a digital camera or a camcorder which is placed on a buoy or stationary mounted in a harbor facility. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them for the presence of marine vehicles. The goal of the system is to detect an approximate window around the ship. The proposed computer vision-based algorithm combines a horizon detection method with edge detection and postprocessing. Several datasets of still images are used to evaluate the performance of the proposed technique. For video sequences the original algorithm is further enhanced with a tracking algorithm that uses Kalman filter. A separate dataset of 30 video sequences 10 seconds each is used to test its performance. Promising results of the detection of ships are discussed and necessary improvements for achieving better performance are suggested.

Visual surveillance in the maritime domain has been explored for more than a decade. Although it has produced a number of working systems and resulted in a mature technology, surveillance has been restricted to the port facilities or areas close to the coastline assuming a fixed-camera scenario. This dissertation presents several contributions in the domain of maritime surveillance. First, a novel algorithm for open-sea visual maritime surveillance is introduced. We explore a challenging situation with a camera mounted on a buoy or other floating platform. The developed algorithm detects, localizes, and tracks ships in the field of view of the camera. Specifically, our method is uniquely designed to handle a rapidly moving camera. Its performance is robust in the presence of a random relatively-large camera motion. In the context of ship detection, a new horizon detection scheme for a complex maritime domain is also developed. Second, the performance of the ship detection algorithm is evaluated on a dataset of 55,000 images. Accuracy of detection of up to 88% of ships is achieved. Lastly, we consider the topic of detection of the vanishing line of the ocean surface plane as a way to estimate the horizon in difficult situations. This allows extension of the ship-detection algorithm to beyond open-sea scenarios.

Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correctly classifying ships. A custom U-Net is implemented to challenge this issue and aims to capture more features in order to provide a more accurate class accuracy. This model is trained with two different systematic architectures: single node architecture and a parameter server variant whose workers act as a boosting mechanism. To ex-tend this effort, a refining method of offline hard example mining aims to improve the accuracy of the trained models in both the validation and target datasets however it results in over correction and a decrease in accuracy. The single node architecture results in 92% class accuracy over the validation dataset and 68% over the target dataset. This exceeds class accuracy scores in related works which reached up to 88%. A parameter server variant results in class accuracy of 86% over the validation set and 73% over the target dataset. The custom U-Net is able to achieve acceptable and high class accuracy on a subset of training data keeping training time and cost low in cloud based solutions. / Graduate

Les radars à synthèse d’ouverture (RSO) polarimétriques sont devenus incontournables dans le domaine de la télédétection, grâce à leur zone de couverture étendue, ainsi que leur capacité à acquérir des données dans n’importe quelles conditions atmosphériques de jour comme de nuit. Au cours des trois dernières décennies, plusieurs RSO polarimétriques ont été utilisés portant une variété de modes d’imagerie, tels que la polarisation unique, la polarisation double et également des modes dits pleinement polarimétriques. Grâce aux recherches récentes, d’autres modes alternatifs, tels que la polarisation hybride et compacte, ont été proposés pour les futures missions RSOs. Toutefois, un débat anime la communauté de la télédétection quant à l’utilité des modes alternatifs et quant au compromis entre la polarimétrie double et la polarimétrie totale. Cette thèse contribue à ce débat en analysant et comparant ces différents modes d’imagerie RSO dans une variété d’applications, avec un accent particulier sur la surveillance maritime (la détection des navires et de marées noires). Pour nos comparaisons, nous considérons un paramètre fondamental, appelé le degré de polarisation (DoP). Ce paramètre scalaire a été reconnu comme l’un des paramètres les plus pertinents pour caractériser les ondes électromagnétiques partiellement polarisées. A l’aide d’une analyse statistique détaillée sur les images polarimétriques RSO, nous proposons des estimateurs efficaces du DoP pour les systèmes d’imagerie cohérente et incohérente. Ainsi, nous étendons la notion de DoP aux différents modes d’imagerie polarimétrique hybride et compacte. Cette étude comparative réalisée dans différents contextes d’application dégage des propriétés permettant de guider le choix parmi les différents modes polarimétriques. Les expériences sont effectuées sur les données polarimétriques provenant du satellite Canadian RADARSAT-2 et le RSO aéroporté Américain AirSAR, couvrant divers types de terrains tels que l’urbain, la végétation et l’océan. Par ailleurs nous réalisons une étude détaillée sur les potentiels du DoP pour la détection et la reconnaissance des marées noires basée sur les acquisitions récentes d’UAVSAR, couvrant la catastrophe de Deepwater Horizon dans le golfe du Mexique. / Polarimetric Synthetic Aperture Radar (SAR) systems have become highly fruitful thanks to their wide area coverage and day and night all-weather capabilities. Several polarimetric SARs have been flown over the last few decades with a variety of polarimetric SAR imaging modes; traditional ones are linear singleand dual-pol modes. More sophisticated ones are full-pol modes. Other alternative modes, such as hybrid and compact dual-pol, have also been recently proposed for future SAR missions. The discussion is vivid across the remote sensing society about both the utility of such alternative modes, and also the trade-off between dual and full polarimetry. This thesis contributes to that discussion by analyzing and comparing different polarimetric SAR modes in a variety of geoscience applications, with a particular focus on maritime monitoring and surveillance. For our comparisons, we make use of a fundamental, physically related discriminator called the Degree of Polarization (DoP). This scalar parameter has been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. Based on a detailed statistical analysis of polarimetric SAR images, we propose efficient estimators of the DoP for both coherent and in-coherent SAR systems. We extend the DoP concept to different hybrid and compact SAR modes and compare the achieved performance with different full-pol methods. We perform a detailed study of vessel detection and oil-spill recognition, based on linear and hybrid/compact dual-pol DoP, using recent data from the Deepwater Horizon oil-spill, acquired by the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Extensive experiments are also performed over various terrain types, such as urban, vegetation, and ocean, using the data acquired by the Canadian RADARSAT-2 and the NASA/JPL Airborne SAR (AirSAR) system.

In our study, sea surface characteristics imaged by multi-polarization space-borne synthetic aperture radar (SAR) have been investigated. For the first time, a decomposition of different scattering mechanisms have been performed for ocean satellite SAR imagery to better understand the non-Bragg (Scalar) contribution to the total radar cross section (RCS) and Doppler measurements. Characteristics retrieval and target classification have been established, using polarimetry and Bayesian detection theories. There are generally three types of surface scattering mechanisms occurring when the sea surface is detected by microwave radar, i.e., Bragg, specular, and Rayleigh. Depolarized Bragg contribution corresponds to sea surface capillary wave, while the other two Scalar contributions correspond respectively to the crest of the longer wave before it breaks and foams formed by wave breaking. Different scattering mechanisms induce different polarimetric scattering coefficients and Doppler spectrum. It had been impossible to separate those scattering mechanisms with single polarization radar imageries. On pixel scale, we decomposed radar scattering matrices physically into Bragg and Scalar contributions. The decomposition is an iteration initiated with the radar incidence angle, and controlled by a local incidence angle which is function of co-polarization and cross-polarization. Based on these developments and testing, a strategy has been refined to analyze the signature of different features, to retrieve wind seas and sea swell parameters, as well as slick areas, ships, oil rigs, such polarized targets that may be buried in the Scalar contribution. With polarimetric scattering matrices estimated both for Bragg and Scalar contributions, a sea clutter model describing almost the real sea surface has been improved statistically. From this point, this improved model could be combined with Bayesian detectors to classify man-made metallic targets, such as ships, oil rigs, etc.