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

共 870 条记录，以下是11-20 订阅

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Boot-Strapping Methods for Improved SAR-GMTI 31

Boot-Strapping Methods for Improved SAR-GMTI

引用

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

Extensive work has been published on theoretical methods for ground moving target indication (GMTI) in synthetic aperture radar (SAR) images. The primary challenge to this problem is that SAR imaging inherently assumes a stationary scene in order to allow a long coherent processing interval, and thus moving targets that violate that assumption may be difficult to reliably detect. Recent work in this area has benefited from experiments with measured data sets, that are of high-quality but also include the measurement imperfections innate to any measured data. In many instances, sophisticated SAR-GMTI techniques have been brought to bear without necessarily employing well-known bootstrapping methods for data calibration and error correction. This leads to comparisons with algorithm baselines that are not reflective of the state of the art and to performance analysis with new algorithms that may be inaccurate or even pessimistic due to the presence of unresolved measurement errors. In this paper, we show that straightforward methods of SAR data calibration allows high-quality SAR-GMTI images to be achieved from measured data via simplistic clutter cancellation and along-track interferometry. These simple steps may serve as the basis for the testing of more advanced algorithms.

关键词： synthetic aperture radar ground moving target indication

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Graph pretraining approach to utilize synthetic data for SAR ATR 31

Graph pretraining approach to utilize synthetic data for SAR...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Parks, Caleb Gauch, Susan Scherreik, Matthew Socha, Ryan Univ Arkansas Fayetteville AR 72701 USA Air Force Res Lab Dayton OH USA

ISBN: (纸本)9781510673830;9781510673823

Because of limitations in availability of synthetic aperture radar (SAR) training data, automatic target recognition (ATR) researchers have turned to the use of synthetic SAR images. Unfortunately, training neural network classifiers on this synthetic data does not yield robust models. Assuming access to limited measured SAR data, we evaluate two natural, transfer-learning approaches to solve this problem, showing that both do not successfully lead to solutions. Motivated by the successes of contrastive, representation, and metric learning, we propose a novel graph-based pretraining approach to transfer knowledge from synthetic samples to real-world scenarios. We show that this approach is applicable to three different neural network architectures obtaining improvements over the baseline approach of 19.21%, 28.70%, and 8.27% respectively. We also demonstrate that our method is robust to the choice of hyperparameters.

关键词： synthetic data transfer learning pretext task

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Efficient and Accurate Approximation of Super-resolution Spectral Estimation Methods for SAR Imaging 31

Efficient and Accurate Approximation of Super-resolution Spe...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Batts, Alex Rigling, Brian Univ Dayton 300 Coll Pk Dayton OH 45469 USA

ISBN: (纸本)9781510673830;9781510673823

Superresolution methods of spectral estimation have shown much promise for creating high quality SAR images that are devoid of the inherent shortcomings of classical Fourier techniques. However, many modern spectral estimation techniques involve the estimation of a correlation matrix and manipulations thereof, both of which can be prohibitively expensive. While efficient methods of computing these spectral estimation techniques have been explored in the literature, for large scenes with high resolution, it is often still too expensive to compute and store the data necessary for the computations. These techniques thus have had limited application in the vast array of SAR imaging problems. This paper presents several methods for generating accurate approximations of these spectral estimation techniques while further improving the computational efficiency and reducing the storage burden.

关键词： synthetic aperture radar Superresolution Spectral Estimation Efficient Computation Efficient Approximation

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Analysis of the Impact of SSIM Parameterization for SAR to EO Translation Networks 31

Analysis of the Impact of SSIM Parameterization for SAR to E...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Weerasinghe, Rajith Young, Eric Shaver, Ryan Ross, Jacob Jamora, J. R. Etegent Technol 2601 Mission Point Suite 220 Beavercreek OH USA Air Force Res Lab Wright Patterson AFB Gate 19B Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

Reconstructing 3D data of objects from limited SAR imagery is of interest due to SARs ability to actively sense targets from a far stand-off range. SAR imagery is non-literal and may not capture the same features as a passive EO camera. However, EO imagery has been shown to be a promising candidate for low-view 3D reconstruction. Thus, a common technique for SAR 3D reconstruction is to first translate a SAR image to an EO image. The structural similarity (SSIM) metric has been shown to be an effective loss function in the techniques used to translate SAR to EO. However, SSIM has several components that can be tuned to achieve optimal performance. This work addresses (i) the parameterization of SSIM for the SAR to EO translation problem and (ii) the ability to reconstruct 3D objects from SAR images after said translations. A parametric sweep is conducted to find optimal parameterization on several matched SAR and EO datasets.

关键词： synthetic aperture radar (SAR) 3D reconstruction SSIM

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Learning to fuse with side information for multi-sensor ATR 31

Learning to fuse with side information for multi-sensor ATR

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Rabin, Zachary Scherreik, Mathew Lewis, Benjamin Davis, Jim Ohio State Univ Columbus OH 43210 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510673830;9781510673823

Multi-sensor fusion algorithms combine information from different sensors to exceed the performance of a single sensor for a given task. In this work, we focus on fusing imagery from electro-optical (EO) and synthetic aperture radar (SAR) sensors for target identification. In addition to the imagery itself, large amounts of metadata or "side information" may be available as well. This data can include important characteristics about the operating conditions (OCs) under which the images were taken. On its own, this metadata is not useful for target identification. However, this extra information can potentially be leveraged to learn better representations of EO and SAR images and improve classification performance. In this work, we assume that side information is available only during training and leverage this information to build contextual deep representations of the target classes. At test time, we fuse the EO and SAR representations to classify the input images without accessing the metadata. We examine the impact of these OC-aware target representations on fusion performance under various forms of OC mismatch between training and testing and show that fusing models trained with side information improves classification accuracy when compared to classifiers trained without side information, especially under more significant train/test OC shifts. We also observe that the inclusion of side information may reduce the trained network's capacity, which implies that side information introduces a regularizing effect. To further study this effect, we empirically compare our approach to classifiers trained with weight decay and bottleneck layers and find that our approach achieves higher accuracy, implying that the inclusion of side information has additional impacts on the learned representations beyond simple regularization.

关键词： synthetic aperture radar Image fusion Sensors Image classification Information fusion

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Dimensionality reduction methods for SAR target recognition 30

Dimensionality reduction methods for SAR target recognition

引用

conference on algorithms for synthetic aperture radar imagery XXX

作者： Lewis, Benjamin Scherreik, Matthew Air Force Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510661547;9781510661554

synthetic aperture radar is an all-weather sensor with many uses, including target recognition. We present work in train a network on synthetic SAR imagery for good performance on measured images. Previous work has used PCA decomposition to a dataset of synthetic and measured SAR imagery for image recognition with initially promising results. This work continues this line of research with kernel PCA using a number of kernels. These techniques are fit using synthetic SAR images, then the measured images are projected into the space at test time. Networks are trained on the lower dimension vectors from the synthetic images and tested on measured images. Performing dimensionality reduction in this way has applications for increased speed of network training and evaluation and in reducing the difference between synthetic and measured domains. We present the results on the publicly available SAMPLE dataset.

关键词： Principal component analysis synthetic aperture radar Automatic target recognition Image classification Feature extraction Target recognition Clutter

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Efficient High Resolution 3D SAR Imaging via Super-resolution Spectral Estimation Methods 31

Efficient High Resolution 3D SAR Imaging via Super-resolutio...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Batts, Alex Rigling, Brian Majumder, Uttam Zelnio, Edmund Univ Dayton 300 Coll Pk Dayton OH 68412 USA Natl Geospatial Intelligence Agcy 7500 GEOINT Dr Springfield VA USA Air Force Res Lab 1864 4th St Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

The volumetric scattering of radar waves allows for three-dimensional SAR imaging of a scene. As is the case in two-dimensions, traditional Fourier methods are easier to implement, but have limitations in terms of quality with respect to speckle, scintillation, and side lobe artifacts. Other methods that have shown promise, such as superresolution methods like the Minimum Variance Method (MVM) and the Multiple Signal Classification (MUSIC) algorithm, have been shown to produce high quality images. However, these algorithms are computationally intense as they require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This paper presents an efficient method for computing these superresolution techniques for 3D SAR images.

关键词： 3D image processing Image resolution Spectral resolution Stereoscopy Super resolution synthetic aperture radar

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synthetic SAR data Domain Randomization for Unseen SAR ATR 31

Synthetic SAR data Domain Randomization for Unseen SAR ATR

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Kim, MinJun Kim, Sungho Yeungnam Univ Gyongsan South Korea

ISBN: (纸本)9781510673830;9781510673823

In recent times, target recognition techniques based on deep learning in the optical domain have exhibited impressive performance. Because of these promising results, there has been a surge in research centered around deep learning in the field of synthetic aperture radar (SAR) target recognition. Most of the contemporary studies directly adopt or modify the deep learning model structures used in optical image target recognition. A primary limitation of this approach is the large amount of data required for training. However, collecting real SAR data entails significant time and cost, and the availability of publicly accessible SAR target datasets are also insufficient. As a solution, studies have been undertaken to generate synthetic SAR data using CAD models and electromagnetic simulations. Yet, a discrepancy in recognition performance emerges due to domain differences between synthetic and real data, especially variations in speckle intensity and side-lobes. In this paper, we propose a novel domain randomization technique to mitigate these inter-domain disparities. Utilizing adversarial generative networks as a foundation, we preserve the core characteristics of SAR targets while minimizing domain differences, applying random transformations to extraneous elements (e.g., Clutter, Speckle). Through this method, we can diversify a single SAR data into various data, effectively augmenting the dataset. This considerably enhances the recognition performance and robustness of deep learning-based target recognitions models.

关键词： synthetic aperture radar Domain Adaptation Domain Randomization Automatic Target Recognition Generative Adversarial Network

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Out-of-distribution detection for SAR imagery using ATR systems 31

Out-of-distribution detection for SAR imagery using ATR syst...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Hill, Charles Etegent Technol 2601 Mission Point Blvd STE 220 Beavercreek OH 45431 USA

ISBN: (纸本)9781510673830;9781510673823

A typical assumption for deploying machine learning models is that the model training and inference data were drawn from the same distribution. However, this assumption rarely holds true for systems deployed in the open world. Inference data can drift over time for numerous reasons, such as changes in operating conditions, adversarial modifications to targets, or sensor degradation. Despite these changes, deep learning models are especially vulnerable to issuing over-confident predictions on out-of-distribution data. This work seeks to address this issue by proposing a framework for describing out-of-distribution detection pipelines, proposing an out-of-distribution detection algorithm using Gaussian Mixture Models which is well suited for SAR ATR, and by evaluating multiple pipelines which exploit the intermediate states of ATR model deep neural networks. This work studies candidate pipelines with varied amounts of dimensionality reduction and detection algorithms on the SAMPLE+ dataset challenge problems for clutter and confuser rejection. Despite the exclusion of out-of-distribution samples from pipeline training, the presented results demonstrate that these samples can nonetheless be reliably detected, exceeding baseline performance by more than 10 percentage points.

关键词： out-of-distribution detection anomaly detection synthetic aperture radar model trust open set recognition artificial intelligence machine learning

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Utilizing SAR imagery in Three-Dimensional Neural Radiance Fields-based Applications 30

Utilizing SAR Imagery in Three-Dimensional Neural Radiance F...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Jamora, J. R. Green, Dylan Talley, Ander Curry, Thomas Air Force Res Lab Wright Patterson AFB OH 45433 USA Dartmouth Coll Hanover NH USA Mississippi State Univ Starkville MS USA Univ North Carolina Charlotte Charlotte NC USA

ISBN: (纸本)9781510661547;9781510661554

Neural Radiance Fields (NeRF) is an emerging technique in the three-dimensional (3D) volumetric representation world due to its ability to learn 3D scenes from sparse two-dimensional (2D) imagery. However, the current implementation focus on electro-optical (EO) representations due to NeRF assumptions with lighting traveling through the scene is absorbed, which is analogous to EO sensor operation. In this work we present a framework for utilizing synthetic aperture radar (SAR) imagery in standard NeRF implementations. Because the physical scattering properties in SAR imagery are markedly different from EO images, we adapt the EO-based transform inputs to equivalent SAR-based parameters. We demonstrate our results on a sample measured SAR dataset with two different 3D SAR reconstruction techniques and demonstrate isotropic scatterer extraction on our sample target.

关键词： synthetic aperture radar neural radiance fields sparse reconstruction object reconstruction

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