Statistical comparison of SAR backscatter from icebergs embedded in sea ice and in open water using RADARSAT-2 images of in Newfoundland waters and the Davis Strait

Icebergs are considered a threat to marine operations. Satellite monitoring of icebergs is one option to aid in the development of iceberg hazard maps. Satellite synthetic aperture radar (SAR) is an obvious choice because of its relative weather independence, day and night operation. Nonetheless, the detection of icebergs in SAR can be a challenge, particularly with high iceberg areal density, heterogeneous background clutter and the presence of sea ice. This thesis investigates and compares polarimetric signatures of icebergs embedded in sea ice and icebergs in open water. In this thesis, RADARSAT-2 images have been used for analysis, which was acquired over locations near the coastline (approximately 3-35 km) of the islands of Newfoundland and Greenland. All icebergs considered here are in the lower incident angle range (below 30 degrees) of the SAR acquisition geometry. For analysis, polarimetry parameters such as co- (HH) and cross- (HV) polarization and several decomposition techniques, specifically Pauli, Freeman-Durden, Yamaguchi, Cloud-Pottier and van Zyl classification, have been used to determine the polarimetric signatures of icebergs and sea ice. Statistical hypothesis tests were used to determine the differences among backscatters from different icebergs. Statistical results tend to show a dominant surface scattering mechanism for icebergs. Moreover, icebergs in open water produce larger volume scatter than icebergs in sea ice, while icebergs in sea ice produce larger surface scatter than icebergs in open water. In addition, there appear to be minor observable differences between icebergs in Greenland and icebergs in Newfoundland

Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico

Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platforms’ backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation

A Depolarization Ratio Anomaly Detector to identify icebergs in sea ice using dual-polarization SAR images

Icebergs represent hazards to maritime traffic and offshore operations. Satellite Synthetic Aperture Radar (SAR) is very valuable for the observation of polar regions and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this work, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross polarization and higher cross- over co-polarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. Firstly, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high resolution data. Subsequently a more exhaustive analysis is carried out using dual-polarization ground detected Sentinel-1a Extra Wide swath images acquired over the time span of two months. The test areas are on the East Coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross polarization channel is carried out exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection

Study of sea clutter influence in ship classification algorithms based on Polarimetric SAR Inteferometry

This paper is focused to evaluate the influence of sea clutter in the performance of ship classification algorithms based on single-pass Polarimetric SAR Interferometry (PolInSAR). For such purpose, series of numerical simulations have been carried out with GRECOSAR, the SAR simulator of complex targets developed by UPC. There, different types of vessels have been considered for a TerraSAR-X like sensor and a sea surface following the two-scale wave approach. The quality of ship discrimination has been quantitatively evaluated with a novel identification method that exploits the particular scattering properties of ships. The results show that the presence of clutter does not notably drop identification performance, despite negative matches can be observed in some particular situations. But the requirement of single-pass interferometric capabilities is not achieved by any of the existing orbital system. This drawback can difficult the validation of what has been observed in simulation environments and can be one of the most limiting factors for the practical implementation of these techniques. Ideas and possible solutions to relax the system requirements are preliminary discussed.Postprint (published version

Automatic vessel monitoring with single and multidimensional SAR images in the wavelet domain

Spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative compared to traditional surveillance methods thanks to the all-weather and day-and-night capabilities of Radar linked with the large coverage of SAR images. Nowadays, the capabilities of satellite based SAR systems are confirmed by a wide amount of applications and experiments all over the world. Nevertheless, specific data exploitation methods are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to present an approach based on multiscale time–frequency analysis for the automatic detection of spots in a noisy background which is a critical matter in a number of SAR applications. The technique has been applied to automatic ship detection in single and multidimensional SAR imagery and it has proven to be a rapid, robust and reliable tool, able to manage complicated heterogeneous scenes where classical approaches may fail.Peer Reviewe

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

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory