Peter McGuire

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Abstract Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidean distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Characterization and statistical modeling of phase in single-channel synthetic aperture radar imagery

Traditionally, the phase content in single-channel synthetic aperture radar (SAR) imagery is discarded. This practice is justified by conventional radar resolution theory, which is a theory strictly relevant to point targets. The advent of high-resolution radars permits small targets previously considered to be points to be now treated as extended targets, in which case this theory is not strictly applicable. With this in mind, this paper offers a new insight into the relevance of phase in single-channel SAR imagery. The proposed approach builds on techniques from the fields of complex-valued and directional statistics. In doing so, three main contributions are presented, the first being a novel method for characterizing the phase content. Second, a new statistical model for the phase is considered, and then a set of 15 solely phase-based features are discussed. Our results are demonstrated on real-world SAR datasets for ground-truthed targets. The statistical significance of the information carried in the phase is clearly demonstrated. Furthermore, if applied to a dataset with higher resolution, the proposed techniques are expected to achieve even higher performance.

A semi-automated technique for internal jugular vein segmentation in ultrasound images using active contours

The assessment of the blood volume is crucial for the management of many acute and chronic diseases. Recent studies have shown that circulating blood volume correlates with the cross-sectional area (CSA) of the internal jugular vein (IJV) estimated from ultrasound imagery. In this paper, a semi-automatic segmentation algorithm is proposed using a combination of region growing and active contour techniques to provide fast and accurate segmentation of IJV ultrasound videos. The algorithm is applied to track and segment the IJV across a range of image qualities, shapes and temporal variation. The experimental results show that the algorithm performs well compared to expert manual segmentation and outperforms several published algorithms incorporating speckle tracking.

Nonlinearity and the effect of detection on single-channel synthetic aperture radar imagery

When signals exhibit non-Gaussian statistics, nonlinear signal processing techniques offer advantages over their linear counterparts. Nonlinearity in high-resolution synthetic aperture radar (SAR) imagery is an intrinsic phenomenon often overlooked in the radar literature. In this paper, we study the nonlinear dynamics, and the effect of detection, in SAR imagery. To this end, two complementary methods for exposing the nonlinear statistics are presented. The first method utilizes histogram fitting with relevant statistical models. The second method is based on hypothesis testing. Our results are demonstrated on real-world Radarsat-2 target chips. It is found that in the presence of extended targets (e.g., ships), the nonlinear effect in the SAR chip is predominant. Nonlinearity is observed to be negligible in the absence of extended targets. As the SAR chip is detected, the nonlinear dynamics are either diminished/altered (i.e., for power-detection) or obliterated (i.e., for magnitude-detection). To take full advantage of nonlinear statistics, it is recommended to utilize the complex-valued SAR image rather than the detected one. Furthermore, the Students T location-scale distribution is seen to offer an excellent model for the SAR chip.

On circularity/noncircularity in single-channel synthetic aperture radar imagery

Motivated by the conventional resolution theory, phase content in single-channel synthetic aperture radar (SAR) imagery is often discarded. In this paper, the validity of this practice is studied from the perspective of complex-valued statistics. Hence, for the phase content to be irrelevant, the complex-valued random variable has to be second-order circular. A procedure to characterize circularity/noncircularity in single-channel SAR imagery is presented. Our analysis is applied to real-world SAR chips from Radarsat-2 and MSTAR. For the case of extended targets, the complex-valued SAR chip is found to be inherently noncircular. Further, the strength of noncircularity is observed to be resolution-dependent. Also, a proportional relationship between noncircularity and nonlinearity is noted. These findings warrant investigating the statistical significance of this phenomenon in relevant target recognition applications.

Recognition of nonlinear dispersive scattering in SAR imagery

This paper presents a new insight into the nonlinear dynamics in SAR imagery. For extended targets, the conventional radar resolution theory is violated due to the nonlinear phase modulation induced by the dispersive scatterers. A novel algorithm motivated by the Hilbert view for the nonlinear phenomenon is introduced. Our algorithm may be used to not only detect the dispersive scatterers but also to estimate the nonlinear order of the phase modulation. Our results are demonstrated on a representative real-world target chip.

Understanding the significance of radiometric calibration for synthetic aperture radar imagery

In applications such as target recognition, quantitative use of the information present in synthetic aperture radar (SAR) imagery is pivotal for detecting and classifying the scattering centers of the target(s). This paper presents an investigation of the various forms for radiometric calibration in SAR imagery. For the cases of point and extended targets, respectively, the radar cross section (σ) and the backscatter coefficient (σ_o) are studied. Other forms of the backscatter coefficient, including the radar brightness (β_o) and (γ_o) are also examined, and their relevance to σ_o is presented. A real-world SAR chip from a single-channel Radarsat-2 image for groundtruthed vehicle targets is used to demonstrate the applicability of the radiometric calibrations. It is concluded that the β_o calibration gives the most accurate result, in contrast to σ_o and γ_o because it is not dependent on the sea-level geoid model typically used to approximate the local incidence angles.

AUV-Based Plume Tracking

This paper presents a simulation study of an autonomous underwater vehicle AUV navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

Holism-based features for target classification in focused and complex-valued synthetic aperture radar imagery

Reductionism and holism are two worldviews underlying the fields of linear and nonlinear signal processing, respectively. Conventional radar resolution theory is motivated by the former view, and it is violated by nonlinear phase modulation induced by dispersive scattering typically associated with extended targets. Motivated by the latter view, this paper offers a new insight into the process of feature extraction for target-recognition applications in single-channel imagery output from synthetic aperture radar processors. Two novel frameworks for holism-based feature extraction are presented. The first framework is based solely on the often-ignored phase chip. The second framework uses the complex-valued 2-D synthetic aperture radar chip after it is transformed into a 1-D vector. Representative features are introduced under each framework. Further, for comparison purposes, baseline features from the power-detected chip are also considered. Three feature sets are extracted from the real-world MSTAR data set and used separately and combinatorially to design multiple instances of an eight-class support vector machine classifier. A classification accuracy of 93.42% is achieved for the holism-based features. This is in comparison to 73.63% for the baseline features. Using Fisher scoring to measure the information contained in each feature, top-ranked features from the first and second holism-based frameworks, respectively, are found to be 7 and 160 times those of the baseline features. Because the nonlinear phenomenon is resolution dependent, our proposed approach is expected to achieve even greater accuracy for synthetic aperture radar sensors with higher resolution.