A notch filter for ship detection with polarimetric SAR data

Ship detection with Synthetic Aperture Radar (SAR) is a major topic for the security and monitoring of maritime areas. One of the advantages of using SAR lay in its capability to acquire useful images with any-weather conditions and at night time. Specifically, this paper proposes a new methodology exploiting polarimetric acquisitions (dual- and quad-polarimetric). The methodology adopted for the detector algorithm was introduced by the author and performs a perturbation analysis in space of polarimetric targets checking for coherence between the target to detect and its perturbed version on the data. In the present work, this methodology is optimized for detection of marine features. In the end, the algorithm can be considered to be a negative (notch) filter focused on sea. Consequently, all the features which have a polarimetric behavior different from the sea are detected (i.e. ships, icebergs, buoys, etc). Moreover, a dual polarimetric version of the detector is designed, to be exploited in the circumstances where quad polarimetric data cannot be acquired. The detector was tested with TerraSAR-X quad polarimetric data showing significant agreement with the available ground truth. Moreover, the theoretical performances of the detector are tested with Monte Carlo simulations in order to extract the probabilities of detection and false alarm. An important result is that the detector is, up to some extend, independent of the sea conditions

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

Analytical modeling, performance analysis, and optimization of polarimetric imaging system

Polarized light can provide additional information about a scene that cannot be obtained directly from intensity or spectral images. Rather than treating the optical field as scalar, polarization images seek to obtain the vector nature of the optical field from the scene. Polarimetry thus has been found to be useful in several applications, including material classification and target detection. Recently, optical polarization has been identified as an emerging technique and has shown promising applications in passive remote sensing. Compared with the traditional spectral content of the scene, polarimetric signatures are much more dependent on the scene geometry and the polarimetric bidirectional reflectance distribution function (pBRDF) of the objects. Passive polarimetric scene simulation has been shown to be helpful in better understanding such phenomenology. However, the combined effects of the scene characteristics, the sensor noise and optical imperfections, and the different processing algorithm implementations on the overall system performance have not been systematically studied. To better understand the effects of various system attributes and help optimize the design and use of polarimetric imaging system, an analytical model has been developed to predict the system performance. A detailed introduction of the analytical model is first presented. The model propagates the first and second order statistics of radiance from a scene model to a sensor model, and finally to a processing model. Validation with data collected from a division of time polarimeter show good agreement between model predictions and measurements. It has been shown that the analytical model is able to predict the general polarization behavior and data trends with different scene geometries. Based on the analytical model we then define several system performance metrics to evaluate the polarimetic signatures of different objects as well as target detection performance. Parameter tradeoff studies have been conducted for analysis of potential system performance. Finally based on the analytical model and system performance metrics we investigate optimal filter configurations to sense polarization. We develop an adaptive polarimetric target detector to determine the optimum analyzer orientations for a multichannel polarization-sensitive optical system. Compared with several conventional operation methods, we find that better target detection performance is achieved with our algorithm

Random Transformations Of Optical Fields And Applications

The interaction of optical waves with material systems often results in complex, seemingly random fields. Because the fluctuations of such fields are typically difficult to analyze, they are regarded as noise to be suppressed. Nevertheless, in many cases the fluctuations of the field result from a linear and deterministic, albeit complicated, interaction between the optical field and the scattering system. As a result, linear systems theory (LST) can be used to frame the scattering problem and highlight situations in which useful information can be extracted from the fluctuations of the scattered field. Three fundamental problems can be posed in LST regardless of the nature of the system: one direct and two inverse problems. The direct problem attempts to predict the response of a known system to a known input. The problem may be simple enough to admit analytical solutions as in the case of homogeneous materials, phase and amplitude screens, and weakly scattering materials; or the problem may require the use of numerical techniques. This dissertation will focus on the two inverse problems, namely the determination of either the excitation field or the scattering system. Traditionally, the excitation determination problem has relied on designing optical systems that respond to the property of interest in a simple, easily quantified way. For example, gratings can be used to map wavelength onto direction of propagation while waveplates and polarizers can map polarization properties onto intensity. The primary difficulty with directly applying the concepts of LST to scattering systems iv is that, while the outputs are still combinations of the inputs, they are not ``simple\u27\u27 combinations such as Fourier transforms or spatially dispersed spectral components of the input spectrum. Instead, the scattered field can be thought of as a massive sampling and mixing of the excitation field. This dissertation will show that such complicated sampling functions can be characterized and that the corresponding scattering medium can then be used as an optical device such as a lens, polarimeter, or spectrometer. The second inverse problem, system determination, is often more difficult because the problem itself may be ill-posed. For scattering systems that are dominated by low-order scattering, the statistical properties of the scattered light may serve as a fingerprint for material discrimination; however, in many situations, the statistical properties of the output do not depend on the material properties. Rather than analyzing the scattered field from one realization of the random interaction, several measurement techniques have been developed that attempt to extract information about the material system from modifications of the scattered field in response to changes in either the excitation or the intrinsic dynamics of the medium itself. One such technique is dynamic light scattering. This dissertation includes an extension to this method that allows for a polarimetric measurement of the scattered light using a reference beam with controllable polarization. Another system determination problem relates to imaging the reflectivity of a target that is being randomly illuminated. It will be demonstrated that an approach based on the correlation between the integrated scattered intensity and the corresponding illumination intensity distribution can prove superior to standard imaging microscop

Exploitation of infrared polarimetric imagery for passive remote sensing applications

Polarimetric infrared imagery has emerged over the past few decades as a candidate technology to detect manmade objects by taking advantage of the fact that smooth materials emit strong polarized electromagnetic waves, which can be remotely sensed by a specialized camera using a rotating polarizer in front of the focal plate array in order to generate the so-called Stokes parameters: S0, S1, S2, and DoLP. Current research in this area has shown the ability of using such variations of these parameters to detect smooth manmade structures in low contrast contrast scenarios. This dissertation proposes and evaluates novel anomaly detection methods for long-wave infrared polarimetric imagery exploitation suited for surveillance applications requiring automatic target detection capability. The targets considered are manmade structures in natural clutter backgrounds under unknown illumination and atmospheric effects. A method based on mathematical morphology is proposed with the intent to enhance the polarimetric Stokes features of manmade structures found in the scene while minimizing its effects on natural clutter. The method suggests that morphology-based algorithms are capable of enhancing the contrast between manmade objects and natural clutter backgrounds, thus, improving the probability of correct detection of manmade objects in the scene. The second method departs from common practices in the polarimetric research community (i.e., using the Stokes vector parameters as input to algorithms) by using instead the raw polarization component imagery (e.g., 0°, 45°, 90°, and 135°) and employing multivariate mathematical statistics to distinguish the two classes of objects. This dissertation unequivocally shows that algorithms based on this new direction significantly outperform the prior art (algorithms based on Stokes parameters and their variants). To support this claim, this dissertation offers an exhaustive data analysis and quantitative comparative study, among the various competing algorithms, using long-wave infrared polarimetric imagery collected outdoor, over several days, under varying weather conditions, geometry of illumination, and diurnal cycles

Photometric characterization of exoplanets using angular and spectral differential imaging

The direct detection of exoplanets has been the subject of intensive research in the recent years. Data obtained with future high-contrast imaging instruments optimized for giant planets direct detection are strongly limited by the speckle noise. Specific observing strategies and data analysis methods, such as angular and spectral differential imaging, are required to attenuate the noise level and possibly detect the faint planet flux. Even though these methods are very efficient at suppressing the speckles, the photometry of the faint planets is dominated by the speckle residuals. The determination of the effective temperature and surface gravity of the detected planets from photometric measurements in different bands is then limited by the photometric error on the planet flux. In this work we investigate this photometric error and the consequences on the determination of the physical parameters of the detected planets. We perform detailed end-to-end simulation with the CAOS-based Software Package for SPHERE to obtain realistic data representing typical observing sequences in Y, J, H and Ks bands with a high contrast imager. The simulated data are used to measure the photometric accuracy as a function of contrast for planets detected with angular and spectral+angular differential methods. We apply this empirical accuracy to study the characterization capabilities of a high-contrast differential imager. We show that the expected photometric performances will allow the detection and characterization of exoplanets down to the Jupiter mass at angular separations of 1.0" and 0.2" respectively around high mass and low mass stars with 2 observations in different filter pairs. We also show that the determination of the planets physical parameters from photometric measurements in different filter pairs is essentialy limited by the error on the determination of the surface gravity.Comment: 13 pages, 7 figures, 4 tables. Accepted for publication in MNRA

Ground-based synthetic aperture radar (GBSAR) interferometry for deformation monitoring

Ph. D ThesisGround-based synthetic aperture radar (GBSAR), together with interferometry, represents a powerful tool for deformation monitoring. GBSAR has inherent flexibility, allowing data to be collected with adjustable temporal resolutions through either continuous or discontinuous mode. The goal of this research is to develop a framework to effectively utilise GBSAR for deformation monitoring in both modes, with the emphasis on accuracy, robustness, and real-time capability. To achieve this goal, advanced Interferometric SAR (InSAR) processing algorithms have been proposed to address existing issues in conventional interferometry for GBSAR deformation monitoring. The proposed interferometric algorithms include a new non-local method for the accurate estimation of coherence and interferometric phase, a new approach to selecting coherent pixels with the aim of maximising the density of selected pixels and optimizing the reliability of time series analysis, and a rigorous model for the correction of atmospheric and repositioning errors. On the basis of these algorithms, two complete interferometric processing chains have been developed: one for continuous and the other for discontinuous GBSAR deformation monitoring. The continuous chain is able to process infinite incoming images in real time and extract the evolution of surface movements through temporally coherent pixels. The discontinuous chain integrates additional automatic coregistration of images and correction of repositioning errors between different campaigns. Successful deformation monitoring applications have been completed, including three continuous (a dune, a bridge, and a coastal cliff) and one discontinuous (a hillside), which have demonstrated the feasibility and effectiveness of the presented algorithms and chains for high-accuracy GBSAR interferometric measurement. Significant deformation signals were detected from the three continuous applications and no deformation from the discontinuous. The achieved results are justified quantitatively via a defined precision indicator for the time series estimation and validated qualitatively via a priori knowledge of these observing sites.China Scholarship Council (CSC), Newcastle Universit

Statistical modeling of polarimetric SAR data: a survey and challenges

Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed.Peer ReviewedPostprint (published version