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

共 871 条记录，以下是51-60 订阅

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LiveEO's Rapid Response Insights: leveraging high-resolution Capella Space SAR imagery to automate near-real-time storm damage detection 15

LiveEO's Rapid Response Insights: leveraging high-resolution...

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15th European conference on synthetic aperture radar, EUSAR 2024

作者： Parisio, Francesco Vicente-Guijalba, Fernando Weinmann, Paul Vancassel, Geoffrey Seidel, Daniel Castelletti, Davide De, Shaunak Cazcarra-Bes, Victor LiveEO Berlin Germany Capella Space San FranciscoCA United States

ISBN: (纸本)9783800762873

LiveEO, in collaboration with Capella Space, has developed an innovative SAR-based post-storm damage detection system: Rapid Response Insight (RRI). Using the enhanced capabilities of Capella Space high resolution SAR satellites, the RRI system offers a rapid response solution with unmatched resolution, coverage and speed. The system will also integrate cutting-edge AI algorithms, automating and optimizing damage detection in post-storm scenarios. The goal of RRI is to reduce downtime of critical infrastructures impacted by storms, delivering high-resolution insights in less than 16 hours from data collection. Our preliminary studies underscored the system's effectiveness in identifying changes in urban and industrial areas. Supporting quick disaster response can improve the resilience of critical infrastructure against the challenges posed by climate change. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Storms

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Template Matching Study on synthetic aperture radar and synthetic aperture LADAR imagery 5

Template Matching Study on Synthetic Aperture RADAR and Synt...

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5th IEEE Research and Applications of Photonics in Defense conference, RAPID 2022

作者： Ross, Jacob W. Raymer, Michael L. Rigling, Brian D. Velten, Vincent J. Wpafb Air Force Research Lab OH United States Wright State University DaytonOH United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

The difference in performance of template matching algorithms on synthetic aperture LADAR and synthetic aperture radar imagery is assessed empirically on a variety of operating conditions and algorithm parameters. Key interactions between algorithm parameters and operating conditions are identified and explored for both modalities. © 2022 IEEE.

关键词： synthetic aperture radar

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Enforcing Feature Correlation on Cycle-Consistent GAN generated functions: A first step in closing the synthetic measured gap found in SAR Images 29

Enforcing Feature Correlation on Cycle-Consistent GAN genera...

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conference on algorithms for synthetic aperture radar imagery XXix

作者： Swan, Matthew Major, Anne Lear, Jacob Parks, Caleb G. Zhan, Justin Univ Arkansas Fayetteville AR 72701 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781510650671;9781510650664

Due to their ability to capture images in a variety of environmental conditions, synthetic aperture radars (SAR) are of particular interest in the automatic target recognition (ATR) domain. In order to develop SAR-ATR machine learning (ML) algorithms, a large sample set indicative of the underlying population must be used. This is an issue since gathering SAR images, even for a single target, is an expensive and time consuming process. Recently a data set, known as the SAMPLE data set, consisting of synthetic SAR samples has been released. Ideally theses synthetic images can be used in place of real SAR samples. Unfortunately, training SAR-ATR ML algorithms with samples exclusively from the SAMPLE data set produces algorithms with poor performance on real SAR images. This paper is focused on creating new variants of cycle-consistent generative adversarial networks (CycleGAN) to produce a transformation function that maps a synthetic SAR image to a useful approximation of a real SAR image. By introducing a new feature correlation module to the cycle consistent GAN architecture we take the first steps in closing the gap between synthetic SAR images and measured SAR images.

关键词： Machine learning Generative adversarial networks Cycle-consistent Generative adversarial networks synthetic aperture radar

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synthetic aperture radar Ship Detection Network Based on Multi-Scale Feature Enhancement 4

Synthetic Aperture Radar Ship Detection Network Based on Mul...

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4th International conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Zhang, Zhixian Tang, Lina Bai, Yin Yang, Siqi Yang, Shuo Wang, Yu China Mobile Chengdu Institute of Research and Development Research and Development Department I Chengdu China

ISBN: (纸本)9798331528973

The paper introduces a ship identification network with SAR imagery, termed Multi-scale Feature enhancement YOLOX (MF-YOLOX), specifically designed to tackle the challenge of poor accuracy in recognizing multi-scale targets under complex circumstances posed by existing algorithms. Within the feature extraction network, the EMA module is incorporated to efficiently encode global spatial information and establish both short-term and long-term dependencies. Furthermore, the CIoU loss function is employed to substitute the original IoU in the prediction network, aiming to mitigate the positional bias issue of the prediction frame and enhance the model's precision in the field of target localization. Experimental outcomes based on the HRSID dataset demonstrate that our approach achieves an F1 score of 89%, an average accuracy mean Average Precision (mAP) of 91%, and a detection speed of 51 Frames Per Second (FPS). Our approach offers the highest detection accuracy and operating efficiency when compared to some advanced methods, providing technical support for precise ship detection in complex maritime conditions. © 2024 IEEE.

关键词： synthetic aperture radar

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Understanding the synthetic and measured GAP from the CNN classifier perspective 29

Understanding the synthetic and measured GAP from the CNN cl...

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conference on algorithms for synthetic aperture radar imagery XXix

作者： Major, Anne Zelnio, Edmund Garber, Frederick Air Force Res Lab Wp Afb OH 45433 USA Wright State Univ Dayton OH 45435 USA

ISBN: (纸本)9781510650671;9781510650664

Many classifier problems are limited by the amount of data that is available to train the algorithm. This problem is exascerbated by deep learners who require large amounts of data. One approach to solving this problem is to use synthetic data to train the classifier;however, as is often the case, there are differences between synthetic and measured data that limit the performance of this approach. This effort baselines a fundamental approach to solving this problem by using a simple Siamese network to classify the target with one twin trained with abundant synthetic SAR data and the other twin trained with limited measured SAR data. The network is trained using the standard cross-entropy cost function and is the functional equivalent of a single network jointly trained by measured and synthetic data. The performance of this approach is characterized as a function of the amount of measured data required to train the algorithm.

关键词： CNN SAR transfer learning synthetic data

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The inversion of ocean current extraction by along-Track interferometric SAR based on sea surface model 3

The inversion of ocean current extraction by along-Track int...

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3rd International conference on Advanced algorithms and Signal Image Processing, AASIP 2023

作者： Wu, Chen Zhao, Rui Yan, He Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9781510668522

It is challenging to estimate ocean surface currents from along-Track interferometric (ATI) data due to the complexity of the ocean environment. In order to complete the process from modeling the ocean surface signals to extracting the ocean surface currents, this research integrates dynamic sea surface modeling imagery with an iterative technique to inverse the currents. To complete the simulation analysis of time-varying ocean surface imaging, ocean surface backscatter coefficients and ocean surface echoes are constructed using the wave spectrum model as the basis. The ATI data from the simulation modeling are inverted using the iterative inversion algorithm of the synthetic aperture radar (SAR) ocean surface currents field, and the accuracy of the inverse ocean surface currents field obtained reaches 0.1 m/s, proving the viability of the whole system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： synthetic aperture radar

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MORPHOLOGICAL CHANGE DETECTION AT PIPELINE CROSSINGS USING REMOTE SENSING: A PROOF-OF-CONCEPT STUDY 14

MORPHOLOGICAL CHANGE DETECTION AT PIPELINE CROSSINGS USING R...

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14th International Pipeline conference (IPC)

作者： Schueder, Rudy Engelbrecht, Jeanine BGC Engn Inc Vancouver BC Canada

ISBN: (纸本)9780791886588

A proof-of-concept research and development study was undertaken to determine to what degree changes in river morphology could be detected from satellite imagery in support of pipeline geohazard monitoring programs. algorithms for the detection of the wetted extent and river centerline were developed in the Python programming language and applied to synthetic aperture radar and multispectral imaging data. Additional algorithms were developed to highlight meaningful changes in wetted extent and river centerline occurring between subsequent data acquisitions. The results showed that it was possible to detect morphological change using satellite imagery, but that the process was effective for rivers with a higher signal to noise ratio represented by the ratio of river width to image pixel size. For smaller rivers (20-50 m), the change in the centerline location was a more useful metric of morphological change. The change detection algorithms were successful at highlighting possibly important changes between subsequent data acquisitions but were limited by the relatively short catalogue of available images to compare changes against. Future work involves the improvement and deployment of the algorithms in operational software systems to support pipeline geohazard management programs. Keywords: Earth Observation, Machine Learning, Computer Vision, Morphological Change Detection, synthetic aperture radar, Multispectral Imaging, Geohazard Management

关键词： Pipelines Imaging synthetic aperture radar Geohazards Rivers algorithms Satellites Computer programming languages Machine learning Computers Computer software Industrial research Signal to noise ratio

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The Guiding Function of Different SAR Image Quality Indicators on Object Detection Tasks 3

The Guiding Function of Different SAR Image Quality Indicato...

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3rd IEEE International conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Zhang, Chongqi Deng, Yao Chong, Mingzhe Feng, Peijie Tan, Yunhua School of Electronics Peking University Beijing China

ISBN: (纸本)9798350380989

In the big data era, synthetic aperture radar (SAR) imagery has become increasingly critical, particularly for data-driven object detection tasks. Various downstream tasks are extremely sensitive to SAR image quality because the SAR image is extremely influenced by diverse factors such as speckle noise. Therefore, the quality assessment of SAR images is crucial, especially in the context of no-reference image quality metrics. However, there is no universal standard to quantitatively assess the SAR image quality, and how these metrics influence the SAR detection tasks is ambiguous. This paper emphasizes the guiding function of different SAR image quality assessment metrics on SAR target detection tasks. A comprehensive comparison of several widely used SAR image quality metrics is undertaken, including entropy, RMS contrast, equivalent number of looks (ENL), Laplacian and Tenengrad sharpness, and frequency domain sharpness. First, utilizing the MSAR large-scale dataset, we analyze these metrics to identify the most suitable ones for object detection tasks. Further, we demonstrate the practical application of these metrics by integrating them into the YOLOv5 object detection framework. This research not only offers a new perspective on SAR image quality assessment but also provides practical guidance for future applications, aligning with the broader need for efficient and accurate no-reference quality assessment methods in areas like remote sensing, where image distortions and the unavailability of reference images are common challenges. © 2024 IEEE.

关键词： object detection SAR image quality metrics synthetic aperture radar (SAR) YOLOv5

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Broadening Circular and Linear SAR image and 3D volume parameters for accurate localization 29

Broadening Circular and Linear SAR image and 3D volume param...

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conference on algorithms for synthetic aperture radar imagery XXix

作者： Pepin, Matthew USAF Albuquerque NM USA

ISBN: (纸本)9781510650671;9781510650664

Determining accurate location with synthetic aperture radar (SAR) images is hindered not only by the limited resolution of the image but also by the non-rectangular nature of the process that produces the image, and the presence of multiple returns from different height objects at the same range. Three dimensional SAR volume measurements can differentiate different height scatterers but imaging methods are limited by the measurement scenario. Interpolation of images and volumes can normalize the collected data but can add inaccuracy to pixel and voxel impulse response (IPR) and localization estimates. Regions of best achievable accuracy define the imaging algorithm performance envelope. Ideal localization occurs at the estimation bound of modeled point scatterers, and is also indicated by pixel and voxel size or IPR main lobe width. The performance envelope of SAR measurement scenarios, radar parameters, and imaging and estimation algorithms can refine system parameters of accurate localization. Use of analogous Circular and Linear SAR (CSAR and LSAR) scenarios enables joint trade-off of flight path along with imaging parameters. An ideal spherical construct underlies joint CSAR and LSAR, two and three dimensional SAR scenarios. This construct also ties CSAR and LSAR imaging to spherical ray-trace creation of phase history. For different scenarios defined through a spherical model of parameters, localization performance of the imaging envelope, IPR and estimation accuracy are examined. Along with sample size and ideal chirp waveform sampling, the azimuth and elevation aperture is varied to include ideal and sub-sampling, and extension to achieve un-aliased images and volumes. Methods expanding the envelope are investigated.

关键词： Imaging arrays synthetic aperture radar 3D modeling 3D image processing Convolution Solid modeling radar 3D acquisition Image resolution

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Multi-Platform EO and SAR Fusion for Target ID 29

Multi-Platform EO and SAR Fusion for Target ID

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conference on algorithms for synthetic aperture radar imagery XXix

作者： Ashby, Mark Zelnio, Edmund Sensors Directorate Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510650671;9781510650664

Electro-optical (EO) sensors and synthetic aperture radars (SAR) have long been used in automatic target recognition (ATR) classifiers. However, maintaining robust target ID performance with ATRs trained on data from a single EO or SAR sensor across a wide range of operating conditions is challenging due to the variability in the sun illumination and surface orientation of targets, respectively. Fusion of ATR classifiers trained on EO and SAR data can improve target ID performance. In this paper, we implement multiple fusion algorithms at the decision level and the feature level using classifiers trained on a standard convolutional neural network (CNN) architecture. Under favorable conditions - when the train and test data have the same operating conditions many fusion algorithms offer significant gain at lower image resolution compared to a single sensor at higher resolution. However, as the operating conditions become more disjoint, only the most robust fusion techniques show significant performance gain. A feature-level fusion algorithm developed by researchers at Sandia National Laboratories and modified for use in this paper showed the most robustness to disjoint operating conditions. Moreover, this algorithm was robust to geometric angle variation and therefore could be used with sensors mounted on different platforms.

关键词： Sensor Fusion synthetic aperture radar (SAR) Automatic Target Recognition (ATR) Target Classification Convolutional Neural Networks (CNN)

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