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检索条件"任意字段=Conference on Algorithms for Synthetic Aperture Radar Imagery XXII"

共 867 条记录，以下是1-10 订阅

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Morphology and Refocus of Fast-Moving Exo-clutter Targets in synthetic aperture radar imagery 31

Morphology and Refocus of Fast-Moving Exo-clutter Targets in...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Carrell, David A. Naval Postgrad Sch Elect & Comp Engn Monterey CA 93943 USA

ISBN: (纸本)9781510673830;9781510673823

The signatures of moving targets with synthetic aperture radar (SAR) imagery are typically smeared primarily in the radar cross-range direction. For such endoclutter targets, the time interval between adjacent radar pulses along the synthetic aperture is sufficiently small that the collection of the target signature does not experience aliasing. If a mobile target is moving sufficiently fast during the SAR collection interval, then the signature exhibits a smearing that lies along a diagonal in the SAR image space of down-range versus cross-range. In the present analysis, the properties of such diagonal signatures are investigated for constant velocity targets. The research further includes the development of new and advanced mathematics and algorithms that yield finely refocused SAR imagery for automatic detection and recognition, even for relatively fast exoclutter targets with non-uniform rotation. This proposal develops and applies two algorithms to detect and refocus fast-moving exoclutter surface targets within SAR imagery: (1) Rapid xoclutter Eocus Transformation (REFT) algorithm to transform input SAR imagery into a form conducive for the detection of fast-moving exoclutter targets, and (2) Arbitrary Rigid Object Motion Autofocus (AROMA) algorithm for automatic focus of moving targets with non-uniform rotation and translation.

关键词： synthetic aperture radar radar imaging moving target imaging

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SAR Image Quantization Strategies for Improved Human/Machine Interpretability 31

SAR Image Quantization Strategies for Improved Human/Machine...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Rigling, Brian D. Majumder, Uttam Zelnio, Edmund Univ Dayton Dept Elect & Comp Engn 300 Coll Pk Dayton OH 45469 USA

ISBN: (纸本)9781510673830;9781510673823

synthetic aperture radar (SAR) images can have broad dynamic range depending on the responses of the content of the observed scenes. This can vary from intense specular responses from manmade objects to low/no-return areas due to shadowing or forward scatter off electromagnetically smooth surfaces. To display SAR images for human consumption or to input them modern recognition algorithms, one must usually adopt a finite precision display. This is typically no more than 8 bits of quantization, but in some instances, utilization of fewer bits may present operational advantages. This paper explores three distinct techniques for SAR image quantization and provides examples with measured imagery.

关键词： synthetic aperture radar radar scattering quantization

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Adversarial Physics-based Augmentations for Robust Training Using synthetic Data 31

Adversarial Physics-based Augmentations for Robust Training ...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Clark, Emma Walters, Ellie Zelnio, Edmund Univ Illinois Urbana IL 60680 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

Given the limited amount of measured synthetic aperture radar data available to train object recognition algorithms. synthetic data is used to train the algorithm while using measured data to test. To account for the variability of measured data and to ensure robustness to various conditions, extensive physics- based augmentations are used during the training process. These augmentations include target, background, and sensor variability. In order to explore the augmentation space most efficiently, the background and sensor variability are explored on-line during the training process using an adversarial learning strategy. Performance trades are reported as a function of the various augmentation strategies.

关键词： synthetic aperture radar Classification

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Using Colorization to Bridge the synthetic-Measured Gap 31

Using Colorization to Bridge the Synthetic-Measured Gap

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Cavallo, Jeremy Rigling, Brian Wayne State Univ Detroit MI 48202 USA Univ Dayton Dayton OH USA

ISBN: (纸本)9781510673830;9781510673823

This paper explores the use of colorization as a data augmentation and its applications in bridging the synthetic-measured gap. A current problem in synthetic aperture radar Automatic Target Recognition (SAR ATR) is training deep learning networks on largely synthetic data and transferring the knowledge to the measured domain. Data augmentations, such as colorization, can make the deep learning models more robust to the shift in domain when used during training, leading to improved performance over traditional synthetic data. Our approach utilizes a lossless colorization augmentation and applies it to various ResNet-based architectures(1) to improve the SAR ATR performance when trained on limited measured data.

关键词： Foundational Models Deep Learning synthetic aperture radar

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Implicit Coding of Scatterer Height and Other Anisotropic Behaviors in Colorized SAR imagery 31

Implicit Coding of Scatterer Height and Other Anisotropic Be...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Rigling, Brian D. Majumder, Uttam Zelnio, Edmund Univ Dayton Dept Elect & Comp Engn 300 Coll Pk Dayton OH 45469 USA

ISBN: (纸本)9781510673830;9781510673823

Conventional synthetic aperture radar processes all of the available k-space data to form a single 2-D image. While this does yield the finest resolution image possible, it implicitly assumes that imaged scatterers lie within the chosen image plane and that their responses are isotropic over the observed k-space. These assumptions neglect out-of-plane height, which can lead to pixel phase and layover variation over the extent of an aperture, and other anisotropic scattering behaviors expected of non-point responses. The averaging process of image formation may therefore be destroying or obscuring data richness that is not easily recovered in later processing. In this paper, we show that subaperture processing of SAR data permits anisotropic scattering behavior, such as out-of-plane height, to be implicitly encoded in color channels, and through a few suggested approaches, we seek to improve image interpretability for humans and machine learning.

关键词： synthetic aperture radar radar scattering

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A Generalized Gamma Copula Model for High Resolution Polarimetric SAR Change Detection 31

A Generalized Gamma Copula Model for High Resolution Polarim...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Hermant, Stephen Ash, Joshua Wright State Univ Dept Elect Engn Dayton OH 45435 USA

ISBN: (纸本)9781510673830;9781510673823

In this paper, we describe a new approach to non-coherent change detection for high resolution polarimetric synthetic aperture radar (polSAR) exploitation. In the high resolution setting, the reduced size of a resolution cell diminishes the applicability of central limit theorem arguments that lead to the traditional Gaussian backscatter models that underpin existing polSAR change detection algorithms. To mitigate this, we introduce a new model for polSAR data that combines generalized Gamma (GG) distributed marginals within a copula framework to capture the correlation dependency between multiple polSAR channels. Using the GG-copula model, a generalized likelihood ratio test (GLRT) is derived for detecting changes within high resolution polSAR imagery. Examples using measured data demonstrate the non-Gaussian nature of high resolution polSAR data and quantify a performance improvement when using the proposed GG-copula change detection framework.

关键词： synthetic aperture radar polarimetry change detection copulas

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Operating Condition Sampling Strategies for Evaluating ATR Performance 31

Operating Condition Sampling Strategies for Evaluating ATR P...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Stephens, Shaun Ross, Jacob Hill, Charles Coleman, William Scherreik, Matthew Etegent Technol Ltd Beavercreek OH 45431 USA Air Force Res Lab Sensors Directorate Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

synthetic data is commonly used to assess the performance of synthetic aperture radar (SAR) Automatic Target Recognition (ATR) systems modeling the OC space in question. In this work we demonstrate that the use of an informed sampling technique compared to an uninformed sampling approach can efficiently assess the "OC gap" between train and test OC spaces as the gap narrows. To demonstrate the effectiveness of an informed sampling approach, SAR ATR experiments are conducted as a function of how representative the train distribution of OCs are compared to the test OC space given a variety of challenging OC scenarios. Algorithm performance is assessed over a series of experiments given discrepancies between azimuth and depression angle of the sensor

关键词： synthetic aperture radar operating conditions automatic target recognition synthetic data

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Empirical analysis of target responses for understanding SAR to EO translations 31

Empirical analysis of target responses for understanding SAR...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Ross, Jacob Shaver, Ryan Young, Eric Weerasinghe, Rajith Jamora, J. R. Etegent Technol Ltd Beavercreek OH USA Air Force Res Lab Sensors Directorate Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

Image-to-image translation methods aim to convert an image from its native source domain to a target domain. This is a common technique when the target domain's phenomenology is more amenable to a certain task than the source domain. An example of this practice is synthetic aperture radar (SAR) to electro-optical (EO) translation for 3D reconstruction. Techniques in 3D reconstruction have been shown to be effective on EO imagery. A common practice is to translate SAR imagery to the EO domain in order to form 3D reconstructions from SAR imagery. The translation algorithms ultimately map specular SAR responses to diffuse EO responses. While previous work supports the effectiveness of deep neural networks for such a translation, the black-box nature of the trained models does not offer explainability towards the effectiveness of the SAR to EO translations. This work aims to offer explainability for SAR to EO translations via direct comparison of facet responses found in ray-tracing based simulations given equivalent target and sensor geometry. Further analysis of these target responses is conducted in order to understand scenarios where SAR to EO translations is expected to be effective and ineffective.

关键词： synthetic aperture radar electro-optical image-to-image translation GAN

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Interferometric Spatial Filter for 3D synthetic aperture radar via Back-projecton Image Formation 31

Interferometric Spatial Filter for 3D Synthetic Aperture Rad...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Shaver, Ryan Ross, Jacob Weerasinghel, Rajith Young, Eric Jamora, J. R. Etegent Technol 2601 Mission Point Blvd Beavercreek OH USA AFRL 2241 Avion Cir Wright Patterson AFB Dayton OH USA

ISBN: (纸本)9781510673830;9781510673823

With the development of airborne synthetic aperture radar (SAR) measurement techniques, 3D SAR image formation has become prevalent in the SAR community. Conventional backprojection algorithms have difficulty mapping scatters to a voxel grid in 3D space due to a myriad of differential ranges in the height dimension. The inaccurate mapping of range profiles is commonly seen in layover defects. To address this issue while using limited sensor aspects, this study utilizes tomographic techniques. Interferometric SAR is leveraged to yield height estimate surfaces and applied to the 3D backprojection images as a spatial filter. Fusion across a swath of aspects in azimuth for a fixed elevation bin are utilized to resolve shadowed and non-resolved features in the surface reconstructions. Height estimations are applied to the 3D image grid corresponding to the range and cross-range voxels. Multiple height estimate algorithms are studied and yield results on a feature level basis of targets accurate within inches for X-Band synthetically generated data.

关键词： synthetic aperture radar (SAR) Interferometric SAR (IFSAR) Tomography 3D Reconstruction

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Using Foundational Models to Bridge the synthetic-Measured Gap 31

Using Foundational Models to Bridge the Synthetic-Measured G...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Cavallo, Jeremy Zelnio, Edmund Wayne State Univ Detroit MI 48202 USA Air Force Res Lab Dayton OH USA

ISBN: (纸本)9781510673830;9781510673823

Due to the differences in the statistical distributions of synthetic versus measured synthetic aperture (SAR) images, it is difficult to train a deep learning model on synthetic images to accurately classify measured images. This research utilizes the enormous computing power required to train foundational models. and approaches the problem from a transfer learning perspective. Since foundational models have been trained on 10's of billions of images, they have feature extraction capabilities far beyond what is possible with standard computational restrictions and greatly reduced data availability. Therefore, we utilize the foundational model's feature extraction capabilities and transfer them to the synthetic-measured gap problem. The hypothesis is that the very rich features resulting from the foundational models trained almost exclusively on EO images can be transferred to the SAR classification problem using synthetic SAR data for training while minimizing the need for measured SAR data.

关键词： Foundational Models Deep Learning synthetic aperture radar

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