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

Ship detection with spectral analysis of synthetic aperture radar: a comparison of new and well-known algorithms

The surveillance of maritime areas with remote sensing is vital for security reasons, as well as for the protection of the environment. Satellite-borne synthetic aperture radar (SAR) offers large-scale surveillance, which is not reliant on solar illumination and is rather independent of weather conditions. The main feature of vessels in SAR images is a higher backscattering compared to the sea background. This peculiarity has led to the development of several ship detectors focused on identifying anomalies in the intensity of SAR images. More recently, different approaches relying on the information kept in the spectrum of a single-look complex (SLC) SAR image were proposed. This paper is focused on two main issues. Firstly, two recently developed sub-look detectors are applied for the first time to ship detection. Secondly, new and well-known ship detection algorithms are compared in order to understand which has the best performance under certain circumstances and if the sub-look analysis improves ship detection. The comparison is done on real SAR data exploiting diversity in frequency and polarization. Specifically, the employed data consist of six RADARSAT-2 fine quad-polacquisitions over the North Sea, five TerraSAR-X HH/VV dual-polarimetric data-takes, also over the North Sea, and one ALOS-PALSAR quad-polarimetric dataset over Tokyo Bay. Simultaneously to the SAR images, validation data were collected, which include the automatic identification system (AIS) position of ships and wind speeds. The results of the analysis show that the performance of the different sub-look algorithms considered here is strongly dependent on polarization, frequency and resolution. Interestingly, these sub-look detectors are able to outperform the classical SAR intensity detector when the sea state is particularly high, leading to a strong clutter contribution. It was also observed that there are situations where the performance improvement thanks to the sub-look analysis is not so noticeable

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

Quad-Polarimetric SAR for Detection and Characterization of Icebergs

Website for ESA Living Planet Symposium 2016.This paper evaluates the performance of fully polarimetric SAR data in iceberg detection and characterization. The study aims to explore the potential of RADARSAT-2 SAR data to detect icebergs and growlers in Svalbard that have broken off from the glaciers nearby. To be able to detect iceberg/growlers in a SAR image, a significant contrast between iceberg and background clutter is required. The sublook cross-correlation magnitude (SCM) is extracted from the complex cross-correlation between subapeture images and contrast between iceberg and sea clutter is measured. The results of target-to-clutter ratio from the SCM indicate that the sublook analysis has an impact on detection performance

Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 -band polarimetric data sets, over San Francisco Bay, and -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico

On the use of the l(2)-norm for texture analysis of polarimetric SAR data

In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.Peer ReviewedPostprint (published version

Statistical tests for a ship detector based on the Polarimetric Notch Filter

Ship detection is an important topic in remote sensing and Synthetic Aperture Radar has a valuable contribution, allowing detection at night time and with almost any weather conditions. Additionally, polarimetry can play a significant role considering its capability to discriminate between different targets. Recently, a new ship detector exploiting polarimetric information was developed, namely the Geometrical Perturbation Polarimetric Notch Filter (GP-PNF). This work is focused on devising two statistical tests for the GP-PNF. The latter allow an automatic and adaptive selection of the detector threshold. Initially, the probability density function (pdf) of the detector is analytically derived. Finally, the Neyman-Pearson (NP) lemma is exploited to set the threshold calculating probabilities using the clutter pdf (i.e. a Constant False Alarm Rate, CFAR) and a likelihood ratio (LR). The goodness of fit of the clutter pdf is tested with four real SAR datasets acquired by the RADARSAT-2 and the TanDEM-X satellites. The former images are quad-polarimetric, while the latter are dual-polarimetric HH/VV. The data are accompanied by the Automatic Identification System (AIS) location of vessels, which facilitates the validation of the detection masks. It can be observed that the pdf's fit the data histograms and they pass the two sample Kolmogorov-Smirnov and χ2 tests

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

Ship detection on open sea and coastal environment

Synthetic Aperture Radar (SAR) is a high-resolution ground-mapping technique with the ability to effectively synthesize a large radar antenna by processing the phase of a smaller radar antenna on a moving platform like an airplane or a satellite. SAR images, due to its properties, have been the focus of many applications such as land and sea monitoring, remote sensing, mapping of surfaces, weather forecasting, among many others. Their relevance is increasing on a daily basis, thus it’s crucial to apply the best suitable method or technique to each type of data collected. Several techniques have been published in the literature so far to enhance automatic ship detection using Synthetic Aperture Radar (SAR) images, like multilook imaging techniques, polarization techniques, Constant False Alarm Rate (CFAR) techniques, Amplitude Change Detection (ACD) techniques among many others. Depending on how the information is gathered and processed, each technique presents different performance and results. Nowadays there are several ongoing SAR missions, and the need to improve ship detection, oil-spills or any kind of sea activity is fundamental to preserve and promote navigation safety as well as constant and accurate monitoring of the surroundings, for example, detection of illegal fishing activities, pollution or drug trafficking. The main objective of this MSc dissertation is to study and implement a set of algorithms for automatic ship detection using SAR images from Sentinel-1 due to its characteristics as well as its ease access. The dissertation organization is as follows: Chapter 1 presents a brief introduction to the theme of this dissertation and its aim, as well as its structure; Chapter 2 summarizes a variety of fundamental key points from historical events and developments to the SAR theory, finishing with a summary of some well-known ship detection methods; Chapter 3 presents a basic guideline to choose the best ship detection technique depending on the data type and operational scenario; Chapter 4 focus on the CFAR technique detailing the implemented algorithms. This technique was selected, given the data set available for testing in this work; Chapter 5 presents the results obtained using the implemented algorithms; Chapter 6 presents the conclusions, final remarks and future work