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检索条件"任意字段=SPIE Conference on Algorithm for Synthetic Aperture Radar Imagery V"

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Near-field SAR imaging with dynamic metasurface antennas using an adapted range migration algorithm 3

Near-field SAR imaging with dynamic metasurface antennas usi...

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conference on Computational Imaging III

作者： Diebold, Aaron v. Pulida-Mancera, Laura Sleasman, Timothy Boyarsk, Michael Imani, Mohammadreza F. Smith, David R. Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781510618503

synthetic aperture radar (SAR) is a well-established approach for retrieving images with high resolution. However, common hardware used for SAR systems is usually complex and costly, and can suffer from lengthy signal acquisition. In near-field imaging, such as through-wall-sensing and security screening, simpler and faster hardware can be found in the form of dynamic metasurface antennas (DMAs). These antennas consist of a waveguide-fed array of tunable metamaterial elements whose overall radiation patterns can be altered by DC signals. By sweeping through a set of tuning states, near-field imaging can be accomplished by multiplexing scene information into a collection of measurements, which are post-processed to retrieve scene information. While DMAs simplify hardware, the post-processing can become cumbersome, especially when DMAs are moving in a fashion similar to SAR. In this presentation, we address this problem by modifying the range migration algorithm (RMA) to be compatible with DMAs. To accommodate complex patterns generated by DMAs in the RMA, a pre-processing step is introduced to transform the measurements into an equivalent set corresponding to an effective multistatic configuration, for which specific forms of the algorithm have been derived. As we are operating in the near field of the antennas, some approximations made in the classical formulation of RMA may not be valid. In this paper, we examine the effect of one such approximation: the discarding of amplitude terms in the signal-target Fourier relationship. We demonstrate the adaptation of the RMA to near field imaging using a DMA as central hardware of a SAR system, and discuss the effects of this approximation on the resulting image quality.

关键词： Computational imaging Metamaterials Image reconstruction techniques

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An Acceleration Framework for synthetic aperture radar algorithms 24

An Acceleration Framework for Synthetic Aperture Radar Algor...

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conference on algorithms for synthetic aperture radar imagery XXIv

作者： Kim, Youngsoo Gloster, Clay S. Alexander, Winser E. San Jose State Univ San Jose CA 95192 USA North Carolina A&T State Univ Greensboro NC USA North Carolina State Univ Raleigh NC 27695 USA

ISBN: (数字)9781510609044

ISBN: (纸本)9781510609037;9781510609044

algorithms for radar signal processing, such as synthetic aperture radar (SAR) are computationally intensive and require considerable execution time on a general purpose processor. Reconfigurable logic can be used to off-load the primary computational kernel onto a custom computing machine in order to reduce execution time by an order of magnitude as compared to kernel execution on a general purpose processor. Specifically, Field Programmable Gate Arrays (FPGAs) can be used to accelerate these kernels using hardware-based custom logic implementations. In this paper, we demonstrate a framework for algorithm acceleration. We used SAR as a case study to illustrate the potential for algorithm acceleration offered by FPGAs. Initially, we profiled the SAR algorithm and implemented a homomorphic filter using a hardware implementation of the natural logarithm. Experimental results show a linear speedup by adding reasonably small processing elements in Field Programmable Gate Array (FPGA) as opposed to using a software implementation running on a typical general purpose processor.

关键词： synthetic aperture radar FPGA Discrete Wavelet Transform

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Deep Learning for SAR Image Formation 24

Deep Learning for SAR Image Formation

引用

conference on algorithms for synthetic aperture radar imagery XXIv

作者： Mason, Eric Yonel, Bariscan Yazici, Birsen Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

ISBN: (数字)9781510609044

ISBN: (纸本)9781510609037;9781510609044

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

关键词： synthetic aperture radar SAR deep learning inverse problems recurrent neural networks RNN radar imaging autofocus neural network

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Adapting range migration techniques for imaging with metasurface antennas: analysis and limitations 24

Adapting range migration techniques for imaging with metasur...

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conference on algorithms for synthetic aperture radar imagery XXIv

作者： Mancera, Laura Pulido Fromenteeze, Thomas Sleasman, Timothy Boyarsky, Michael Imani, Mohammadreza F. Reynolds, Matthew S. Smith, David R. Duke Univ Ctr Metamat & Integrated Plasmon Dept Elect & Comp Engn Durham NC 27708 USA XLIM Res Inst Limoges France Univ Washington Dept Elect Engn Seattle WA 98195 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

ISBN: (数字)9781510609044

ISBN: (纸本)9781510609037;9781510609044

Dynamic metasurface antennas are planar structures that exhibit remarkable capabilities in controlling electromagnetic wave-fronts, advantages which are particularly attractive for microwave imaging. These antennas exhibit strong frequency dispersion and produce diverse radiation patterns. Such behavior presents unique challenges for integration with conventional imaging algorithms. We analyze an adapted version of the range migration algorithm (RMA) for use with dynamic metasurfaces in image reconstruction. Focusing on the the proposed pre-processing step, that ultimately allows a fast processing of the backscattered signal in the spatial frequency domain from which the fast Fourier transform can efficiently reconstruct the scene. Numerical studies illustrate imaging performance using both conventional methods and the adapted RMA, demonstrating that the RMA can reconstruct images with comparable quality in a fraction of the time. In this paper, we demonstrate the capabilities of the algorithm as a fast reconstruction tool, and we analyze the limitations of the presented technique in terms of image quality.

关键词： Metamaterials synthetic aperture radar (SAR) Microwave Imaging Range Migration algorithm

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A Novel Latent Gaussian Copula Framework for Modeling Spatial Correlation in Quantized SAR imagery with Applications to ATR 24

A Novel Latent Gaussian Copula Framework for Modeling Spatia...

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conference on algorithms for synthetic aperture radar imagery XXIv

作者： Thelen, Brian J. Xique, Ismael J. Burns, Joseph W. Goley, G. Steven Nolan, Adam R. Benson, Jonathan W. Michigan Tech Res Inst 3600 Green Court Ann Arbor MI 48105 USA Etegent Technol 3000 Presidential Dr Suite 340 Fairborn OH 45324 USA Leidos 3745 Pentagon Blvd Beavercreek OH 45431 USA

ISBN: (数字)9781510609044

ISBN: (纸本)9781510609037;9781510609044

With all of the new remote sensing modalities available, and with ever increasing capabilities and frequency of collection, there is a desire to fundamentally understand/quantify the information content in the collected image data relative to various exploitation goals, such as detection/classification. A fundamental approach for this is the framework of Bayesian decision theory, but a daunting challenge is to have significantly flexible and accurate multivariate models for the features and/or pixels that capture a wide assortment of distributions and dependencies. In addition, data can come in the form of both continuous and discrete representations, where the latter is often generated based on considerations of robustness to imaging conditions and occlusions/degradations. In this paper we propose a novel suite of "latent" models fundamentally based on multivariate Gaussian copula models that can be used for quantized data from SAR imagery. For this Latent Gaussian Copula (LGC) model, we derive an approximate, maximum-likelihood estimation algorithm and demonstrate very reasonable estimation performance even for the larger images with many pixels. However applying these LGC models to large dimensions/images within a Bayesian decision/classification theory is infeasible due to the computational/numerical issues in evaluating the true full likelihood, and we propose an alternative class of novel pseudo-likelihoood detection statistics that are computationally feasible. We show in a few simple examples that these statistics have the potential to provide very good and robust detection/classification performance. All of this framework is demonstrated on a simulated SLICY data set, and the results show the importance of modeling the dependencies, and of utilizing the pseudo-likelihood methods.

关键词： Gaussian Copula Likelihood pseudo-likelihood Bayesian classification Discrimination

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A DEEP NEURAL NETWORK FOR OIL SPILL SEMANTIC SEGMENTATION IN SAR IMAGES

A DEEP NEURAL NETWORK FOR OIL SPILL SEMANTIC SEGMENTATION IN...

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IEEE International conference on Image Processing

作者： Georgios Orfanidis Konstantinos Ioannidis Konstantinos Avgerinakis Stefanos vrochidis Ioannis Kompatsiaris Centre for Research and Technology Hellas (CERTH)-Information Technologies Institute (ITI)

Oil spills pose a major threat of the oceanic and coastal environments, hence, an automatic detection and a continuous monitoring system comprises an appealing option for minimizing the response time of relevant operations. Numerous efforts have been conducted towards such solutions by exploiting a variety of sensing systems such as satellite synthetic aperture radar (SAR) which can identify oil spills over sea surfaces in any environmental conditions and operational time. Such approaches include the use of artificial neural networks which effectively identify the polluted areas. Considering their remarkable abilities in many applications, deep Convolutional Neural Networks (DCNN) could surpass limitations and performances of previously proposed methods. This paper describes the application of an approach that combines the merits of a DCNN with SAR imagery in order to provide a fully automated oil spill identification system. The model semantically segments the input SAR images into multiple areas of interest. The deployed DCNN was trained using multiple SAR images acquired from the Sentinel-1 satellite provided by ESA and based on EMSA records for maritime pollution events. Experiments on such challenging benchmark datasets for such an abstract problem demonstrate that the algorithm can accurately identify oil spills leading to an effective detection solution.

关键词： Oils Feature extraction synthetic aperture radar Image segmentation Satellites Pollution Convolution

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Oil spill characterization thanks to optical airborne imagery during the NOFO campaign 2015

Oil spill characterization thanks to optical airborne imager...

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conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions

作者： viallefont-Robinet, F. Ceamanos, X. Angelliaume, S. Miegebielle, v. Off Natl Etud & Rech Aerosp 2 Ave E BelinBP 74025 F-31055 Toulouse 4 France Off Natl Etud & Rech Aerosp BA 701 F-13661 Salon Air France CSTJF Rech TOTAL 6 Ave Larribau F-64018 Pau France

ISBN: (纸本)9781510613096;9781510613089

One of the objectives of the NAOMI (New Advanced Observation Method Integration) research project, fruit of a partnership between Total and ONERA, is to work on the detection, the quantification and the characterization of offshore hydrocarbon at the sea surface using airborne remote sensing. In this framework, work has been done to characterize the spectral signature of hydrocarbons in lab in order to build a database of oil spectral signatures. The main objective of this database is to provide spectral libraries for data processing algorithms to be applied to airborne vNIRSWIR hyperspectral images. A campaign run by the NOFO institute (Norwegian Clean Seas Association for Operating Companies) took place in 2015 to test anti-pollution equipment. During this campaign, several hydrocarbon products, including an oil emulsion, were released into the sea, off the Norwegian coast. The NOFO team allowed the NAOMI project to acquire data over the resulting oil slicks using the SETHI system, which is an airborne remote sensing imaging system developed by ONERA. SETHI integrates a new generation of optoelectronic and radar payloads and can operate over a wide range of frequency bands. SETHI is a pod-based system operating onboard a Falcon 20 Dassault aircraft, which is owned by AvDEF. For these experiments, imaging sensors were constituted by 2 synthetic aperture radar (SAR), working at X and L bands in a full polarimetric mode (HH, Hv, vH, vv) and 2 HySpex hyperspectral cameras working in the vNIR (0,4 to 1 mu m) and SWIR (1 to 2,5 mu m) spectral ranges. A sample of the oil emulsion that was used during the campaign was sent to our laboratory for analysis. Measurements of its transmission and of its reflectance in the vNIR and SWIR spectral domains have been performed at ONERA with a Perkin Elmer spectroradiometer and a spectrogoniometer. Several samples of the oil emulsion were prepared in order to measure spectral variations according to oil thickness, illumination angle

关键词： Hydrocarbons detection Hydrocarbons spectral signature offshore remote sensing spectral matching hydrocarbon emulsion

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Convolutional Neural Networks for synthetic aperture radar Classification 23

Convolutional Neural Networks for Synthetic Aperture Radar C...

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conference on algorithms for synthetic aperture radar imagery XXIII

作者： Profeta, Andrew Rodriguez, Andres Clouse, H. Scott Wright State Univ Dayton OH 45435 USA Intel Corp Hillsboro OR 97124 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510600843

For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

关键词： synthetic aperture radar radar Neural networks Convolutional neural networks algorithm development Machine learning Automatic target recognition Network architectures Detection and tracking algorithms Speckle

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Superpixel-guided CFAR detection of ships at sea in SAR imagery

Superpixel-guided CFAR detection of ships at sea in SAR imag...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Odysseas A. Pappas Alin M. Achim David R. Bull Visual Information Laboratory University of Bristol BS8 1UB UK

ISBN: (纸本)9781509041183

synthetic aperture radar (SAR) has over the years evolved to be one of the most promising remote sensing modalities for large-scale monitoring of the ocean and maritime activity. The detection of ships at sea in SAR imagery is a challenging task, as it requires the detection of small targets with little exploitable spatial information within a high resolution image. We present a novel method for the detection of ships based on superpixel segmentation and subsequent statistical characterisation, with no prior land masking. Our method acts as a bound to a CFAR detector, greatly reducing false positives. We present results on SENTINEL-1 imagery, demonstrating the detection performance of our algorithm.

关键词： synthetic aperture radar Superpixels CFAR Detection

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Performance Prediction of Multinomial Pattern Matching Under Ideal Point Response variations 23

Performance Prediction of Multinomial Pattern Matching Under...

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conference on algorithms for synthetic aperture radar imagery XXIII

作者： Horvath, Matthew S. Rigling, Brian D. Wright State Univ Dept Elect Engn 3640 Colonel Glenn Highway Dayton OH 45435 USA

ISBN: (纸本)9781510600843

Typical ATR performance metrics are based on the results of empirical studies on truthed datasets where it is difficult to fully sample the space of expected variation yielding potentially false generalizations of empirical performance results to a rigorous performance assessment. This is especially difficult when many sources of variation can exist in the data, typically referred to as operating conditions. Here, we propose a general method to analytically predict the classification performance of the MPM algorithm when samples are assumed realizations of two separate MPM template parametrizations differing as a function of a single, conditionally independent operation condition. This performance prediction approach is then used to investigate the role the ideal point response has in the classification performance of synthetic aperture radar targets. The exact trade-off we study is coherently processing an aperture to yield a single higher resolution image versus non-coherently processing the aperture to yield multiple lower resolution looks of a scene. Experiments are conducted using SAR imagery from the Air Force Research Laboratories Civilian vehicle dataset. An additional performance analysis presents an analytic approach to predict algorithm performance under additive white Gaussian noise for a general Nq allowing the performance loss under IPR variations to be mapped to an equivalent loss in signal-to-noise ratio.

关键词： Image processing Image resolution Signal to noise ratio synthetic aperture radar Quantization Statistical analysis Detection and tracking algorithms Sensors Scattering Automatic target recognition

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