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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21-30 订阅

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Efficient Computation of Superresolution Methods for SAR Imaging 30

Efficient Computation of Superresolution Methods for SAR Ima...

引用

conference on algorithms for synthetic aperture radar imagery XXX

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

ISBN: (纸本)9781510661547;9781510661554

While traditional Fourier methods of SAR imaging are well known in addition to being easy to implement, they have limitations in terms of quality, particularly with respect to speckle, scintillation, and side lobe artifacts. Methods of SAR imaging that have shown promise include superresolution methods like the Minimum Variance Method (MVM) and the Multiple Signal Classification (MUSIC) algorithm;however, these algorithms are computationally intense. Both algorithms 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 estimating the correlation matrix and shows how the structure of the correlation matrix can be exploited to efficiently compute the aforementioned superresolution methods.

关键词： synthetic aperture radar Minimum Variance Method MUSIC Algorithm Efficient Computation

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Novel view synthesis with compressed sensing as data augmentation for SAR ATR 30

Novel view synthesis with compressed sensing as data augment...

引用

conference on algorithms for synthetic aperture radar imagery XXX

作者： Banas, Katherine M. Hill, Tyler A. Kreucher, Chris Raeker, Brian O. Simpson, Kyle Weeks, Kirk KBR Inc 900 Victors Way Ann Arbor MI 48108 USA Signature Res Inc 2045 Fountain Profess Ct Navarre FL 32566 USA

ISBN: (纸本)9781510661547;9781510661554

This work investigates the application of compressed sensing algorithms to the problem of novel view synthesis in synthetic aperture radar (SAR). We demonstrate the ability to generate new images of a SAR target from a sparse set of looks at said target, and we show that this can be used as a data augmentation technique for deep learning-based automatic target recognition (ATR). The newly synthesized views can be used both to enlarge the original, sparse training set, and in transfer learning as a source dataset for initial training of the network. The success of the approach is quantified by measuring ATR performance on the MSTAR dataset

关键词： novel view synthesis data augmentation synthetic aperture radar automatic target recognition basis pursuit denoising transfer learning deep learning MSTAR dataset

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synthetic aperture radar Physics-based Image Randomization for Identification Training - SPIRIT 30

Synthetic Aperture Radar Physics-based Image Randomization f...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Clark, Emma Zelnio, Edmund Univ Illinois Champaign IL 60680 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510661547;9781510661554

Accurate classifications of air-to-ground targets of interest is extremely important. Measured data is expensive and difficult to gather for training deep learning networks. By creating synthetic images that can train deep learning networks to classify measured images, the effort and money needed for training deep learning networks for target classification is greatly reduced. This effort addresses a key technical challenge associated with training a deep learning network by augmenting a limited set of measured data with synthetic synthetic aperture radar (SAR) data to train a deep learning network to classify military tactical vehicles. To account for the differences between synthetic and measured SAR data, this effort performs extensive data augmentation using synthetic data to create target and background variability. The goal is to create variability in a physically realistic way so that high classification performance is achieved when training with synthetic data. In addition, architecture modifications are also investigated to assess their contribution to performance.

关键词： SAR CNN Deep learning training

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Enhanced Compressed Sensing 3D SAR Imaging via Cross-Modality EO-SAR Joint-Sparsity Priors 30

Enhanced Compressed Sensing 3D SAR Imaging via Cross-Modalit...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Rajagopal, Abhejit Hilton, Jason Boutte, David Brown, Andrew P. Jamora, Jan R. Toyon Res Corp Goleta CA 93117 USA Univ Calif San Francisco San Francisco CA 94158 USA Air Force Res Lab Dayton OH USA

ISBN: (纸本)9781510661547;9781510661554

We introduce a compressed sensing technique for leveraging prior electro-optic (EO) imagery to improve 3D synthetic aperture radar (SAR) imaging performance. Specifically, we build on existing iterative reconstruction algorithms by guiding the reconstruction process with a joint-sparsity regularization term that captures the complementary structural information shared between EO and SAR via a sparsifying transform in the 3D image domain. We demonstrate this approach using the wavelet transform, the non-uniform Fast-Fourier transform (NUFFT), and optimizers built on autograd utilizing the 2004 AFRL Gotcha SAR dataset, with complementary EO imagery collected from the 2013 Minor Area Motion imagery (MAMI) collection and more recent (2016) satellite collections over the same area. Results indicate significant improvements in 2D and 3D imaging performance via incorporation of the cross-modality EO prior, which we attribute to the convex problem formulation.

关键词： synthetic aperture radar imaging 3D reconstruction multi-modality compressed sensing priors

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Leveraging structural information for enhanced coherent change detection 31

Leveraging structural information for enhanced coherent chan...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Dayton, Scott Milledge, Oliver Nikitovic, Jovana Gelb, Anne Green, Dylan Viswanathan, Aditya Dartmouth Coll 27 N Main St Hanover NH 03755 USA Univ Michigan Dearborn 4901 Evergreen Rd Dearborn MI 48128 USA

ISBN: (纸本)9781510673830;9781510673823

We consider the two-pass coherent change detection problem for SAR imaging. Inspired by classical maximum likelihood-based coherent change detectors (Jakowatz, 1996)(1) and multi-polarization SAR change detection techniques (Novak, 2005),(2) we propose a method of incorporating underlying structural image information using specially formulated kernels. In particular, we utilize a class of convolutional edge detection kernels to extract underlying edge information in the scene of interest given noisy and potentially incomplete data. We then adapt existing multi-polarization SAR change detection methods to incorporate such edge information to improve the quality and robustness of resulting change maps. We validate the proposed method using real-world SAR images from the CCD Challenge Problem dataset and demonstrate improved change detection performance using empirical ROC studies.

关键词： coherent change detection synthetic aperture radar edge detection GOTCHA

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synthetic Data, Measured Data Integrated Learning Experiments 30

Synthetic Data, Measured Data Integrated Learning Experiment...

引用

conference on algorithms for synthetic aperture radar imagery XXX

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

ISBN: (纸本)9781510661547;9781510661554

This paper addresses the problem of adequately training deep learning networks to be operational on measured synthetic aperture radar (SAR) data when the quantity of measured data alone is insufficient. In particular, this is a study in transfer learning utilizing synthetically generated SAR data and measured SAR data to train a deep learning algorithm to classify military tactical vehicles. The present study is motivated by sparsity of measured data for Air Force targets of interest. Specifically, this effort builds on an existing Convolution Neural Network (CNN) architecture, i.e. Understanding the synthetic and Measured GAP from the CNN Classifier Perspective(1) and aims to improve achievable performance by increasing the algorithm complexity and performing parameter analysis on MSTAR data.

关键词： Transfer Learning CNN SAR Classification

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Signature Analysis in synthetic aperture radar imagery with a radar Target Simulator 9

Signature Analysis in Synthetic Aperture Radar Imagery with ...

引用

conference on Target and Background Signatures ix

作者： Amarandi-Netedu, Lidia-Marta Merz, Marcel Progin, Olivier Nagy, Laurent Caris, Michael Wellig, Peter Henke, Daniel Dominguez, Elias Mendez Univ Zurich Winterthurerstr 190 CH-8057 Zurich Switzerland Armasuisse W T Feuerwerkerstr 39 CH-3602 Thun Switzerland Fraunhofer FHR Fraunhoferstr 20 D-53343 Wachtberg Germany

ISBN: (数字)9781510667020

ISBN: (纸本)9781510667013

Artificially inserted objects in synthetic aperture radar (SAR) images are an important component of modern electronic warfare, e.g. for the concealment or illusion of targets. Target simulation can be achieved through software and hardware techniques, by means of a radar target simulator (RTS). In this work, we designed an experiment involving an RTS on the ground and a synthetic aperture radar (SAR) mounted on an aircraft. The study aims to assess the performance of the RTS, analyse measured RTS signals in SAR imagery and its usefulness for future missions. The SAR sensor was the Fraunhofer's (FHR) MIRANDA35 operating at Ka-band with signals of 600 MHz bandwidth. The RTS can simulate an adjustable number of targets, intensity, and slant range positions. During the experiments, the RTS was observed from different viewing angles, depending on the trajectory of the aircraft carrying MIRANDA35. The RTS signals were generated using the delay-based technique, so the target's location in the focused SAR image varied depending on the time delay. Five corner reflectors were placed on the ground, enabling a comparison of the RTS signatures with those of the fixed reflectors. The theoretical position and backscatter of the simulated targets, based on the RTS configuration, were compared to those measured in the SAR images. The results showed that the measured and theoretical slant range values differed by about 2 meters.

关键词： Concealed target artificially inserted object simulated target synthetic aperture radar radar slant range

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algorithms for efficient multi-temporal change detection in SAR imagery 30

Algorithms for efficient multi-temporal change detection in ...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Allen, Michael Kosianka, Justyna W. Perillo, Mark Ursa Space Syst 130 E Seneca St 520 Ithaca NY 14850 USA

ISBN: (纸本)9781510661547;9781510661554

Advancements in the tasking and collection capabilities of SAR providers have reduced the spatiotemporal constraints on SAR-based change detection. As data constraints are relaxed, pairwise SAR-based change detection algorithms are rapidly becoming insufficient for summarizing change activity. To address this, we present two multi-temporal change detection algorithms that categorize, filter, and reduce the changes detected in any number of repeat pass SAR scenes: (1) Object Permanence change detection (OPcd);and (2) Activity Exclusion change detection (AEcd). When compared to traditional pairwise change detection methods, OPcd and AEcd allow for rapid digestion and efficient visualization of changes.

关键词： SAR Change Detection Time Series Forest Change Deforestation

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Decision Level Fusion Experiments on MWIR, VNIR, and SAR imagery 30

Decision Level Fusion Experiments on MWIR, VNIR, and SAR Ima...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Ross, Jacob Stephens, Shaun Bischof, Patrick Nolan, Adam Scherreik, Matthew Etegent Technol Beavercreek OH 45431 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781510661547;9781510661554

Decision level fusion algorithms combine separate classification scores of a test sample to make a unified class declaration. The aim of decision level fusion algorithms is to achieve better classification performance by combining decisions rather than picking the single best performing algorithm. Multi-modal fusion can be achieved by fusing scores of deep learning models trained on different sensing modalities and tested on a target imaged from co-located sensors. Given differences in phenomenology, fusing EO sensors with SAR may boost performance when extended operating conditions are detrimental to the performance of one modality over the other. The EO modalities discussed in this work (VNIR and MWIR) are susceptible to the time of day while SAR is robust to time of day. Conversely, SAR returns of a target can vary greatly when aspect angle changes while EO modalities are relatively robust. This work analyzes the effectiveness of decision level fusion algorithms on MWIR, VNIR, and SAR modalities given disparate times of day and collection aspects.

关键词： synthetic aperture radar Performance modeling Sensors Automatic target recognition Data fusion

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Improving SAR ATR using synthetic data via transfer learning 30

Improving SAR ATR using synthetic data via transfer learning

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Raeker, Brian O. Hill, Tyler A. Kreucher, Chris Banas, Katherine M. Tactac, Kevin Simpson, Kyle Weeks, Kirk KBR Inc 900 Victors Way Ann Arbor MI 48108 USA Signature Res Inc 2045 Fountain Profess CT Navarre FL 32566 USA

ISBN: (纸本)9781510661547;9781510661554

Attempts to use synthetic data to augment measured data for improved synthetic aperture radar (SAR) automatic target recognition (ATR) performance have been hampered by domain mismatch between datasets. Past work which leveraged synthetic data in a transfer learning framework has been successful but was primarily focused on transferring generic SAR features. Recently SAMPLE, a paired synthetic and measured dataset was introduced to the SAR community, enabling demonstration of good ATR performance using 100% synthetic data. In this work, we examine how to leverage synthetic data and measured data to boost ATR using transfer learning. The synthetic dataset corresponds to the MSTAR 15 degrees dataset. We demonstrate that high quality synthetic data can enhance ATR performance even when substantial measured data is available, and that synthetic data can reduce measured data requirements by over 50% while maintaining classification accuracy.

关键词： transfer learning synthetic aperture radar automatic target recognition MSTAR dataset SAMPLE dataset deep learning

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