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

Holism for target recognition in synthetic aperture radar imagery

Reductionism and holism are two worldviews that underlie the fields of linear and nonlinear signal processing, respectively. In the reductionist worldview, deviation from linearity is seen as a noise that warrants removal. In the holistic worldview, the system is viewed as a whole that cannot be fully understood solely in terms of its constituent parts. Conventional radar resolution theory is a direct application of the reductionist view. Consequently, analysis of single-channel synthetic aperture radar imagery for automatic target recognition (SAR-ATR) has traditionally been based on linear techniques associated with the image intensity while the phase content is ignored. The insufficiency of the linear system theory to extended targets has been empirically observed in the literature. This thesis consists of a development of novel tools that exploit the nonlinear phenomenon in focused single-channel SAR imagery and application of these tools to the SAR-ATR problem. A systematic procedure to infer the statistical significance of the nonlinear dynamics is introduced. Furthermore, two novel frameworks for feature extraction from complex-valued SAR imagery are presented. The first framework is solely based on the often ignored phase content, and it is built on techniques from the fields of complex-valued and directional statistics. The second framework utilizes complexvalued SAR imagery and provides for exploiting nonlinear and nonstationary signal analysis methods based on the Poincaré and Hilbert views for nonlinear phenomena. Using real-world SAR datasets, the overall results confirm the statistical significance of the nonlinear effect for the case of extended targets. Furthermore, when the complexvalued SAR image is detected, the nonlinear dynamics are found to be obliterated or greatly altered. The efficacy of the frameworks developed is clearly demonstrated

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

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 Euclidian 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

Unscrambling Nonlinear Dynamics in Synthetic Aperture Radar Imagery

In analyzing single-channel synthetic aperture radar (SAR) imagery, three interrelated questions often arise. First, should one use the detected or the complex-valued image? Second, what is the `best' statistical model? Finally, what constitutes the `best' signal processing methods? This paper addresses these questions from the overarching perspective of the generalized central limit theorem, which underpins nonlinear signal processing. A novel procedure for characterizing the nonlinear dynamics in SAR imagery is proposed. To apply the procedure, three complementary 1-D abstractions for a 2-D SAR chip are introduced. Our analysis is demonstrated on real-world datasets from multiple SAR sensors. The nonlinear dynamics are found to be resolution dependent. As the SAR chip is detected, nonlinear effects are found to be obliterated (i.e., for magnitude-detection) or altered (i.e., for power-detection). In the presence of extended targets (i.e., nonlinear scatterers), it is recommended to use the complex-valued chip rather than the detected one. Furthermore, to exploit the intrinsic nonlinear statistics, it is advised to utilize relevant nonlinear signal analysis techniques

AMBIGUITY DETECTION IN REPEAT-PASS SHIP DETECTION MODE RCM IMAGERY

False alarms (FAs) pose a major challenge for operational ship detection in spaceborne Synthetic Aperture Radar (SAR) imagery. This paper presents a novel algorithm for identification and tracking of FAs in repeat-pass Radar Constellation Mission (RCM) imagery. The algorithm benefits from that not only ambiguities but also fixed ocean structures and small islands are recurrently detected at predetermined geographic locations in the repeat-pass acquisitions. Efficacy of the proposed algorithm is demonstrated through tracking an azimuth ambiguity, a small island, and an elevation grating lobe range ambiguity in a stack of twenty-six Ship Detection RCM images acquired near Galapagos Islands.  </p

Reliability Modeling of Wireless Sensor Network for Oil and Gas Pipelines Monitoring

Extensive network of pipelines carrying oil and gas is an integral part of any country’s energy management plan. As oil and gas are characterized as highly hazardous, their transportation through pipelines warrants proactive continuous monitoring. Unfortunately, there has been limited continuous monitoring of this crucial infrastructure, which causes financial losses to the industry. This paper presents a wireless sensor network (WSN) system and its reliability assessment model for oil and gas pipelines condition monitoring. As a first step, a wireless sensor system for pipeline monitoring is selected. The selected system is revised for oil and gas application considering long distance transportation. Upon system development, a reliability model for the system is developed. A simple bottom-up approach is followed to analyze the reliability of the components, subsystem, and the system. Two explanatory examples are presented to demonstrate the applications of the selected system and developed reliability model. These examples help to better understand the interrelation between the reliabilities, the components and the system