The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Khalid El-darymli - One of the best experts on this subject based on the ideXlab platform.
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Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review
IEEE Access, 2016Co-Authors: Khalid El-darymli, Eric W. Gill, Peter Mcguire, Desmond Power, Cecilia MoloneyAbstract: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
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Holism for target recognition in synthetic aperture Radar Imagery
2015Co-Authors: Khalid El-darymliAbstract: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 Poincare 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.
Clifford L. Trump - One of the best experts on this subject based on the ideXlab platform.
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Estimating ocean frontal surface velocity distributions from Radar Imagery signatures
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: A.l. Cooper, S.r. Chubb, Mark A. Sletten, Clifford L. TrumpAbstract:An inversion algorithm for inferring the surface velocity field of buoyant plume frontal features from observed Radar Imagery has been developed. The inversion technique is based upon an assumption, suggested by Alpers and Hennings' (AH) relaxation model (1984), that near strongly convergent fronts, the Radar cross-section should be proportional to the component of the local current gradient that is directed along the Radar-look direction. However, at X-band, the technique only works when wave-breaking (WB) effects, which are not included in the AH model, are incorporated. This WB model successfully reproduces the magnitude of the signature in images of the plume front at higher frequencies (X-band), where it is known that the AH model is deficient. WB effects play a dominant ro/spl circ/le in the backscatter associated with frontal regions with strong surface convergence fields. These results suggest that the enhancements of Radar backscatter in the vicinity of strongly-convergent fronts are proportional to the local current-convergence but that the underlying scattering process involves WB in a manner that cannot be understood from the AH model. Results are presented for the estimated velocity field derived from Radar Imagery of the Chesapeake Bay plume front. Preliminary considerations of the convergence and uniqueness of the inversion technique are extended by means of a controlled numerical experiment involving the inversion of a prescribed input velocity field.
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Estimating ocean frontal surface velocity distributions from Radar Imagery signatures
2001Co-Authors: A.l. Cooper, S.r. Chubb, Mark A. Sletten, Clifford L. TrumpAbstract:An inversion algorithm for inferring the surface velocity field of buoyant plume frontal features from observed Radar Imagery has been developed [7]-[9]. The inversion technique is based upon an assumption, suggested by Alpers and Hennings' (AH) relaxation model [1], that near strongly convergent fronts, the Radar cross-section (RCS) should be proportional to the component of the local current gradient that is directed along the Radar-look direction. However, at X-band, the technique only works when wave-breaking (WB) effects, which are not included in AH model, are incorporated using a recently developed model [2], [3]. This WB model successfully reproduces the magnitude of the signature in images of the plume front at higher frequencies (X-band), where it is known [3]-[5] that the AH model is deficient. WB effects play a dominant role in the backscatter associated with frontal regions with strong surface convergence fields. These results suggest that the enhancements of Radar backscatter in the vicinity of strongly-convergent fronts are proportional to the local current-convergence but that the underlying scattering process involves WB in a manner that cannot be understood from the AH model. Results are presented for the estimated velocity field derived from Radar Imagery of the Chesapeake Bay plume front [6]. Preliminary considerations [7] of the convergence and uniqueness of the inversion technique are extended by means of a controlled numerical experiment involving the inversion of a prescribed input velocity field.
Cecilia Moloney - One of the best experts on this subject based on the ideXlab platform.
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Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review
IEEE Access, 2016Co-Authors: Khalid El-darymli, Eric W. Gill, Peter Mcguire, Desmond Power, Cecilia MoloneyAbstract: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
Veronique Miegebielle - One of the best experts on this subject based on the ideXlab platform.
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Multifrequency Radar Imagery and characterization of hazardous and noxious substances at sea. V2
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Sebastien Angelliaume, Brent Minchew, Sophie Chataing, Philippe Martineau, Veronique MiegebielleAbstract:The increase in maritime traffic, particularly the transport of hazardous and noxious substances (HNSs), requires advanced methods of identification and characterization in environmental chemical spills. Knowledge about HNS monitoring using Radar remote sensing is not as extensive as for oil spills; however, any progress on this issue would likely advance the monitoring of both chemical and oil-related incidents. To address the need for HNS monitoring, an experiment was conducted in May 2015 over the Mediterranean Sea during which controlled releases of HNS were imaged by a multifrequency Radar system. The aim of this experiment was to establish a procedure for collecting evidence of illegal maritime pollution by noxious liquid substances using airborne Radar sensors. In this paper, we demonstrate the ability of Radar Imagery to detect and characterize chemicals at sea. A normalized polarization difference parameter is introduced to quantify both the impacts of released product on the ocean surface and the relative concentration of the substance within the spill. We show that Radar Imagery can provide knowledge of the involved HNS. In particular, one can distinguish a product that forms a film on the top of the sea surface from another that mixes with seawater, the information that is critical for efficient cleanup operations.
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Multifrequency Radar Imagery and Characterization of Hazardous and Noxious Substances at Sea
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Sebastien Angelliaume, Brent Minchew, Sophie Chataing, Philippe Martineau, Veronique MiegebielleAbstract:The increase in maritime traffic, particularly the transport of hazardous and noxious substances (HNSs), requires advanced methods of identification and characterization in environmental chemical spills. Knowledge about HNS monitoring using Radar remote sensing is not as extensive as for oil spills; however, any progress on this issue would likely advance the monitoring of both chemical and oil-related incidents. To address the need for HNS monitoring, an experiment was conducted in May 2015 over the Mediterranean Sea during which controlled releases of HNS were imaged by a multifrequency Radar system. The aim of this experiment was to establish a procedure for collecting evidence of illegal maritime pollution by noxious liquid substances using airborne Radar sensors. In this paper, we demonstrate the ability of Radar Imagery to detect and characterize chemicals at sea. A normalized polarization difference parameter is introduced to quantify both the impacts of released product on the ocean surface and the relative concentration of the substance within the spill. We show that Radar Imagery can provide knowledge of the involved HNS. In particular, one can distinguish a product that forms a film on the top of the sea surface from another that mixes with seawater, the information that is critical for efficient cleanup operations.
A.l. Cooper - One of the best experts on this subject based on the ideXlab platform.
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Estimating ocean frontal surface velocity distributions from Radar Imagery signatures
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: A.l. Cooper, S.r. Chubb, Mark A. Sletten, Clifford L. TrumpAbstract:An inversion algorithm for inferring the surface velocity field of buoyant plume frontal features from observed Radar Imagery has been developed. The inversion technique is based upon an assumption, suggested by Alpers and Hennings' (AH) relaxation model (1984), that near strongly convergent fronts, the Radar cross-section should be proportional to the component of the local current gradient that is directed along the Radar-look direction. However, at X-band, the technique only works when wave-breaking (WB) effects, which are not included in the AH model, are incorporated. This WB model successfully reproduces the magnitude of the signature in images of the plume front at higher frequencies (X-band), where it is known that the AH model is deficient. WB effects play a dominant ro/spl circ/le in the backscatter associated with frontal regions with strong surface convergence fields. These results suggest that the enhancements of Radar backscatter in the vicinity of strongly-convergent fronts are proportional to the local current-convergence but that the underlying scattering process involves WB in a manner that cannot be understood from the AH model. Results are presented for the estimated velocity field derived from Radar Imagery of the Chesapeake Bay plume front. Preliminary considerations of the convergence and uniqueness of the inversion technique are extended by means of a controlled numerical experiment involving the inversion of a prescribed input velocity field.
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Estimating ocean frontal surface velocity distributions from Radar Imagery signatures
2001Co-Authors: A.l. Cooper, S.r. Chubb, Mark A. Sletten, Clifford L. TrumpAbstract:An inversion algorithm for inferring the surface velocity field of buoyant plume frontal features from observed Radar Imagery has been developed [7]-[9]. The inversion technique is based upon an assumption, suggested by Alpers and Hennings' (AH) relaxation model [1], that near strongly convergent fronts, the Radar cross-section (RCS) should be proportional to the component of the local current gradient that is directed along the Radar-look direction. However, at X-band, the technique only works when wave-breaking (WB) effects, which are not included in AH model, are incorporated using a recently developed model [2], [3]. This WB model successfully reproduces the magnitude of the signature in images of the plume front at higher frequencies (X-band), where it is known [3]-[5] that the AH model is deficient. WB effects play a dominant role in the backscatter associated with frontal regions with strong surface convergence fields. These results suggest that the enhancements of Radar backscatter in the vicinity of strongly-convergent fronts are proportional to the local current-convergence but that the underlying scattering process involves WB in a manner that cannot be understood from the AH model. Results are presented for the estimated velocity field derived from Radar Imagery of the Chesapeake Bay plume front [6]. Preliminary considerations [7] of the convergence and uniqueness of the inversion technique are extended by means of a controlled numerical experiment involving the inversion of a prescribed input velocity field.
