CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions (GΓDs). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust

A New Form of the Polarimetric Notch Filter

Ship detection using polarimetric synthetic radar (PolSAR) imagery attracts a lot of attention in recent years. Most notably, the detector polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in PolSAR imagery, which gives excellent performances. In this work, a mathematical form of one new PNF (NPNF) based on physical mechanisms of targets and clutter is further developed for partial targets. The different mechanisms have been revealed based on the projection matrix. The experimental results including simulated and measured data demonstrate that the NPNF exhibits a better performance than the original PNF

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

PolSAR Ship Detection Based on Neighborhood Polarimetric Covariance Matrix

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms

Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed that performs well in most environments. In this study, a novel subspace form of the PDOF (SPDOF) was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed to the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target clutter ratio (TCR) case

Quad polarimetric synthetic aperture radar analysis of icebergs in Greenland and Svalbard

Polarimetric synthetic aperture radar (PolSAR) has been widely used in ocean and cryospheric applications. This is because, PolSAR can be used in all-day operations and in areas of cloud cover, and therefore can provide valuable large-scale monitoring in polar regions, which is very helpful to shipping and offshore maritime operations. In the last decades, attention has turned to the potential of PolSAR to detect icebergs in the Arctic since they are a major hazard to vessels. However, there is a substantial lack of literature exploring the potentialities of PolSAR and the understanding of iceberg scattering mechanisms. Additionally, it is not known if high resolution PolSAR can be used to detect icebergs smaller than 120 metres. This thesis aims to improve the knowledge of the use of PolSAR scattering mechanisms of icebergs, and detection of small icebergs. First, an introduction to PolSAR is outlined in chapter two, and monitoring of icebergs is presented in chapter three. The first data chapter (Chapter 4) is focused on developing a multi-scale analysis of icebergs using parameters from the Cloude-Pottier and the Yamaguchi decompositions, the polarimetric span and the Pauli scattering vector. This method is carried out using ALOS-2 PALSAR quad polarimetric L-band SAR on icebergs in Greenland. This approach outlines the good potential for using PolSAR for future iceberg classification. One of the main important outcomes is that icebergs are composed by a combination of single targets, which therefore may require a more complex way of processing SAR data to properly extract physical information. In chapter five, the problem of detecting icebergs is addressed by introducing six state-of-the-art detectors previously applied to vessel monitoring. These detectors are the Dual Intensity Polarisation Ratio Anomaly Detector (iDPolRAD), Polarimetric Notch Filter (PNF), Polarimetric Matched Filter (PMF), reflection symmetry (sym), Optimal Polarimetric Detector (OPD) and the Polarimetric Whitening Filter (PWF). Cloude-Pottier entropy, and first and third eigenvalues (eig1 and eig3) of the coherency matrix are also utilised as parameters for comparison. This approach uses the same ALOS-2 dataset, but also evaluates detection performance in two scenarios: icebergs in open ocean, and in sea ice. Polarimetric modes (quad-pol, dual-pol, and single intensities) are also considered for comparison. Currently it is very difficult to detect icebergs less than 120 metres in length using this approach, due to the scattering mechanisms of icebergs and sea ice being very similar. However, it was possible to obtain detection performances of the OPD and PWF, which both showed a Probability of Detection (PF) of 0.99 when the Probability of False Alarms (PF) was set to 10-5 in open ocean. Similarly, in dual pol images, the PWF gave the best performance with a PD of 0.90. Results in sea ice found eig3 to be the best detector with a PD of 0.90 while in dual-pol mode, iDPolRAD gave a PD of 0.978. Single intensity detector performance found the HV channel gave the best detection with a PD of 0.99 in open ocean and 0.87 in sea ice. In the previous two approaches, only satellite data is used. However, in chapter six, data from a ground-based Ku-band Gamma Portable Radio Interferometer (GPRI) instrument is introduced, providing images that are synchronised with the satellite acquisitions. In this approach, the same six detectors are applied to three multitemporal RADARSAT-2 quad pol C-band SAR images on icebergs in Kongsfjorden, Svalbard to evaluate the detection performance within a changing fjord environment. As before, we also make use of Cloude-Pottier entropy, eig1 and eig3. Finally, we evaluate the target-to-clutter ratio (TCR) of the icebergs and check for correlation between the backscattering coefficients and the iceberg dimension. The results obtained from this thesis present original additions to the literature that contributes to the understanding of PolSAR in cryospheric applications. Although these methods are applied to PolSAR and ground-based radar on vessels, they have been applied for the first time on icebergs in this thesis. To summarise, the main findings are that icebergs cannot be represented as single or partial targets, but they do exhibit a collection of single targets clustered together. This result leads to the fact that entropy is not sufficient as a parameter to detect icebergs. Detection results show that the OPD and PWF detectors perform best in an open ocean setting and using quad-pol mode. These results are degraded in dual-pol mode, while single intensity detection is best in the HV cross polarisation channel. When these detectors are applied to the RADARSAT-2 in Svalbard, the OPD and PWF detectors also perform best with PD values ranging between 0.5-0.75 for a PF of 0.01-0.05. However, the sea ice present in the fjord degrades performance across all detectors. Correlation plots with iceberg size show that a regression is not straightforward and Computer Vision methodologies may work best for this