Sea target detection using spaceborne GNSS-R delay-doppler maps: theory and experimental proof of concept using TDS-1 data

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This study addresses a novel application of global navigation satellite system-reflectometry (GNSS-R) delay-Doppler maps (DDMs), namely sea target detection. In contrast with other competing remote sensing technologies, such as synthetic aperture radar and optical systems, typically exploited in the field of sea target detection, GNSS-R systems could be employed as satellite constellations, so as to fulfill the temporal requirements for near real-time ships and sea ice sheets monitoring. In this study, the revisit time offered by GNSS-R systems is quantitatively evaluated by means of a simulation analysis, in which three different realistic GNSS-R missions are simulated and analyzed. Then, a sea target detection algorithm from spaceborne GNSS-R DDMs is described and assessed. The algorithm is based on a sea clutter compensation step and uses an adaptive threshold to take into account spatial variations in the sea background and/or noise statistics. Finally, the sea target detector algorithm is tested and validated for the first time ever using experimental GNSS-R data from the U.K. TechDemoSat-1 dataset. Performance is assessed by providing the receiver operating characteristic curves, and some preliminary experimental results are presented.Peer ReviewedPostprint (published version

AIS-based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

International audienceThis paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detec- tion algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (Automatic Identification System) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Scattering Models in Remote Sensing: Application to SAR Despeckling and Sea Target Detection from GNSS-R Imagery

Imaging sensors are an essential tool for the observation of the Earth’ surface and the study of other celestial bodies. The capability to produce radar images of the illuminated surface is strictly related with the complex phenomenology of the radiation-matter interaction. The electromagnetic scattering theory is a well-established and well-assessed topic in electromagnetics. However, its usage in the remote sensing field is not adequately investigated and studied. This Ph.D. Thesis addresses the exploitation of electromagnetic scattering models suitable for natural surfaces in two applications of remotely sensed data, namely despeckling of synthetic aperture radar (SAR) imagery, and the detection of sea targets in delay-Doppler Maps (DDM) acquired from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R). The first issue was addressed by conceiving, developing, implementing and validating two despeckling algorithms for SAR images. The developed algorithms introduce some a priori information about the electromagnetic behavior of the resolution cell in the despeckling chain and were conceived as a scattering-based version of pre-existing filters, namely the Probabilistic Patch-Based (PPB) and SAR-Block-Matching 3-D (SARBM3D) algorithms. The scattering behavior of the sensed surface is modeled assuming a fractal surface roughness and using the Small Perturbation Method (SPM) to describe the radar cross section (RCS) of the surface. Performances of the proposed algorithms have been assessed using both canonical test (simulated) and actual images acquired from the COSMO\SkyMed constellation. The robustness of the proposed filters against different error sources, such as the scattering behavior of the surface, surface parameters, Digital Elevation Model (DEM) resolution and the SAR image-DEM coregistration step, has been evaluated via an experimental sensitivity analysis. The problem of detecting sea targets from GNSS-R data in near real-time has been investigated by analyzing the revisit time achieved by constellations of GNSS-R instruments. A statistical analysis of the global revisit time has been performed by means of mission simulation, in which three realistic scenario have been defined. Time requirements for near real-time ship detection purposes are shown to be fulfilled in multi-GNSS constellation scenarios. A four-step sea target has been developed. The detector is a Constant False Alarm Rate (CFAR) algorithm and is based on the suppression of the sea clutter contribution, modeled via the Geometrical Optics (GO) approach. Performance assessment is performed by deriving the Receiver Operating Curves (ROC) of the detector. Finally, the proposed sea target detection algorithm has been tested using actual UK TechDemoSat-1 data

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

The Second Spaceborne Imaging Radar Symposium

Summaries of the papers presented at the Second Spaceborne Imaging Radar Symposium are presented. The purpose of the symposium was to present an overwiew of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans