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

共 871 条记录，以下是81-90 订阅

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Complex-Valued Neural Networks for synthetic aperture radar Image Classification

Complex-Valued Neural Networks for Synthetic Aperture Radar ...

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IEEE radar conference (radarConf)

作者： Scarnati, Theresa Lewis, Benjamin Qualis Corp Huntsville AL 35806 USA US Air Force Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781728176093

synthetic aperture radar (SAR) is an imaging modality used for a variety of military and civilian tasks, many of which could benefit greatly from computer automation. The increase in machine learning-based computer vision techniques in recent years has created a number of helpful methods to advance this goal, but most of these algorithms are designed for electro-optical RGB imagery. Applying these off-the-shelf algorithms to magnitude only SAR imagery has shown promise in tasks such as automatic target recognition (ATR). However, very few algorithms exploit the complex-valued nature of radar data. We present a survey of several complex neural network techniques as applied to a SAR data set consisting of military targets. We comment on the merits of each approach and demonstrate the accuracy of each technique when 1) training data are limited, and 2) when the training and testing data exhibit a domain mismatch.

关键词： synthetic aperture radar complex networks machine learning

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

LiveEO’s Rapid Response Insights: leveraging high-resolutio...

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EUSAR 2024;15th European conference on synthetic aperture radar

作者： Francesco Parisio Fernando Vicente-Guijalba Paul Weinmann Geoffrey Vancassel Daniel Seidel Davide Castelletti Shaunak De Victor Cazcarra-Bes

ISBN: (纸本)9783800762866

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.

关键词： Climate change radar polarimetry synthetic aperture radar interferometry synthetic aperture radar radar imaging Artificial intelligence Storms Weather forecasting Critical infrastructure Satellite images Disaster management Data models Industrial facilities Urban areas

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Detecting Ship Wake from Complex Backgrounds in synthetic aperture radar (SAR) imagery

Detecting Ship Wake from Complex Backgrounds in Synthetic Ap...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International conference on

作者： Ajisha Mathias Gubba Pradeepthi Ongole Vivek Kummara Kishore Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Ship wake identification is crucial for analyzing ocean surface synthetic aperture radar (SAR) pictures because ship wakes typically include important vessel information. A significant proportion of detection algorithms make use of the linear traits of ship wakes by converting the lines in the spatial domain into either dark or light points in a transform domain, using techniques like the Hough or Radon transforms. This study presents a novel approach for detecting ship wakes using a Gaussian Mixture Model (GMM). The method involves obtaining the Radon transform of the SAR image, which enhances the visibility of linear features. In comparison to the state of art methodologies, the findings show that the method is successful in achieving greater wake detection accuracies.

关键词： Technological innovation Sea surface Surveillance Transforms radar polarimetry Safety Security Marine vehicles synthetic aperture radar Gaussian mixture model

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Divide-and-Conquer Variational Bayesian Inference for Multi-task Learning of High-resolution SAR imagery

Divide-and-Conquer Variational Bayesian Inference for Multi-...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lei Yang Ming Sun Zhongwei Hu Zenan Zhang Wenxuan Yuan College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Conventional statistical-driven synthetic aperture radar (SAR) imaging algorithms can only encode a single and/or static prior, leading to that limited features can be accessed quantitatively. To this end, a novel multi-task learning framework is proposed by devising a divide-and-conquer variational Bayesian (DC-VB) inference, so that elaborated features of interests can be exploited for a high-resolution SAR imagery. Specifically, a flexible generalized Gaussian distribution (GGD) and a customized hybrid probability distribution are introduced and employed for the priors of features of interests. To resolve the resultant complicated Bayesian inference, splitted random variables are incorporated, so that the joint posterior problem can be decomposed into multiple local problems that are easy to be solved. Simultaneously, dual variables are established for residual errors of the decomposition. To guarantee a global solution for the image of the target of interests, the data augmentation is employed to coordinate multiple local solutions. Therefore, the intended Bayesian inference works in a divide-and-conquer manner, which is superior in quantization of multiple features of high-resolution SAR imagery. It is capable of incorporating multiple priors in a fully-statistical probability and guaranteeing closed-form solutions of posterior distributions. Unavoidable propagation errors can be minimized in the DC-VB process. Raw SAR data is applied to validate the effectiveness of the proposed algorithm. Comparisons with conventions show the superiority in terms of qualitative and quantitative aspects.

关键词： Quantization (signal) Closed-form solutions Signal processing algorithms Multitasking Data augmentation radar polarimetry Inference algorithms Bayes methods Speech processing synthetic aperture radar

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synthetic aperture radar Image Ship Detection Based on YOLO-SARshipNet 1

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11th International conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Chen, Bing Zhu, Yuting Wang, Chen Wang, Xiaoqing Transport Planning and Research Institute Ministry of Transport Beijing China Electronics and Communication Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9789819912605

ISBN: (纸本)9789819912599

Automatic ship detection from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR ship detection methods based on the convolution neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based methods and spatial-domain-based methods exhibit certain limitations. Some algorithms do not consider the detection speed and model scale when improving the detection accuracy, which limits the real-time application and deployment of SAR. To solve this problem, a lightweight, high-speed and high-accurate SAR ship detection method based on yolov3 has been proposed. First, the backbone part of the model is improved, the pure integer quantization network is applied as the core to reduce a small amount of accuracy while reducing the model scale by more than half;Second, modify the feature pyramid network to improve the detection performance of small-scale ships by enhancing feature receptive fields;third, introduce the IoU loss branch to further improve the detection and positioning accuracy;finally, the feature distillation is applied to handle the problem of accuracy decrease caused by model integer quantization. The experimental results on the two public SAR ship datasets show that this algorithm has certain practical significance in the real-time SAR application, and its lightweight parameters are helpful for future FPGA or DSP hardware transplantation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Distillation

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Oil Pipeline Leak Detection Using Deep Learning: A Review on POC Implementation

Oil Pipeline Leak Detection Using Deep Learning: A Review on...

引用

2023 Middle East Oil, Gas and Geosciences Show, MEOS 2023

作者： AlAzri, Ahmed Alkaabi, Sultan AlZakwani, Saud Altamimi, Fadhil AlMamari, Maadh AlSulaimani, Mohammed PDO

ISBN: (纸本)9781613999806

Oil and gas production operations are key sources of environmental pollution which exposing the people and effect the human activity in the world. Petroleum Development Oman (PDO) is the leading exploration and production oil and gas companies in the Sultanate of Oman which lead to avoid adverse health effects in Oman. Oil pipline leakes could be undetected for a long time. However, the precise methods could help improve the oil leaking detection response process in channel required resources with more effectively to be concerned regions. Existing synthetic-aperture radar (SAR) approaches are limited by their algorithm complexity which difficult to work with imbalanced data sets, doubts to select optimal features, and the relatively slow detection. Using deep learning approach could speed up the oil detection. convolutional neural network U-Net segmentation models based on oil leaking detection could be achieve promising automated results. However, there are insufficient features extraction due to loss of target to detect oil leaking or shadows in drone images that commonly appear in various size, shapes, and brightness levels, which the images that captured under many conditions. To overcome all these limitations, we utilized a deep learning model named Pyramid Scene Parsing Network (PSPNet). The proposed algorithm can probabilistically detect oil leak from drone imagery at the frequency of data collection. Thus, PDO Oman could reduce millions of Dollars when direct action from operators that received a quick true alarm of oil leaking. The effectiveness of the proposed method is demonstrated through A proof of concept (POC) based on a realistic dataset that collected history data that our deep learning algorithms achieved the perfect predict the oil leaking before occurs. Copyright © 2023, Society of Petroleum Engineers.

关键词： synthetic aperture radar

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Attention-Augmented YOLO11 for High-Precision Aircraft Detection in synthetic aperture radar imagery

Attention-Augmented YOLO11 for High-Precision Aircraft Detec...

引用

Digital Signal Processing and its Applications (DSPA), International conference on

作者： Irem Bayraktar Murat Bakirci Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

synthetic aperture radar (SAR) imaging has emerged as a pivotal technology in remote sensing, offering unparalleled capabilities for high-resolution imaging under all-weather conditions, day or night. This versatility has rendered SAR indispensable for various critical applications, including ground-based aircraft monitoring. Despite its advantages, the complex nature of SAR imagery, characterized by speckle noise and intricate geometric distortions, presents significant challenges for object detection tasks. In response, advanced object detection algorithms have become essential for extracting actionable insights from SAR data. This study focuses on ground-based aircraft detection using the latest addition to the YOLO family, YOLO11, a state-of-the-art object detection algorithm. While YOLO11 exhibits cutting-edge performance in standard detection tasks, its built-in attention mechanisms were found insufficient for addressing the unique complexities of SAR imagery. To overcome this limitation, we augmented the neck architecture of YOLO11 with Enhanced Channel Attention (ECA) modules, achieving significant performance improvements. The ECA-enhanced YOLO11 demonstrated an average increase in precision of 1.7% and mAP50 of 2.30% across various input image resolutions, with gains of up to 3.4% in lower-resolution scenarios $({256}\times {256}\ \text{pixels})$ . Furthermore, the F1 score consistently improved by up to 2.40%, showcasing the model's ability to balance precision and recall effectively. The results highlight the potential of integrating ECA modules into detection architectures to mitigate the challenges of SAR imagery, making it possible to achieve more reliable and accurate aircraft detection under complex imaging conditions.

关键词： Attention mechanisms Computational modeling Object detection Computer architecture Military aircraft radar polarimetry Complexity theory Air traffic control Aircraft synthetic aperture radar

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Phenomenology-informed techniques for machine learning with measured and synthetic SAR imagery 28

Phenomenology-informed techniques for machine learning with ...

引用

conference on algorithms for synthetic aperture radar imagery XXVIII

作者： Walker, Christopher P. Larson, Kelsie M. Erteza, Ireena A. Bray, Brian K. Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (数字)9781510642942

ISBN: (纸本)9781510642942

Phenomenology-Informed (PI) Machine Learning is introduced to address the unique challenges faced when applying modern machine-learning object recognition techniques to the SAR domain. PI-ML includes a collection of data normalization and augmentation techniques inspired by successful SAR ATR algorithms designed to bridge the gap between simulated and real-world SAR data for use in training Convolutional Neural Networks (CNNs) and other architectures that perform well in the low-noise, feature-dense space of camera-based imagery. The efficacy of PI-ML will be evaluated using ResNet, EfficientNet, and other networks, using both traditional training techniques and all-SAR transfer learning.

关键词： SAR ATR MSTAR Phenomenology-Informed Machine Learning Transfer Learning Simulated SAR Data

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Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR imagery 4

Hybrid Feature Extraction Based on PCA and CNN for Oil Rig C...

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conference on Artificial Intelligence and Machine Learning in Defense Applications IV

作者： da Silva, Fabiano G. Ramos, Lucas P. Palm, Bruna G. Alves, Dimas I. Pettersson, Mats I. Machado, Renato Aeronaut Inst Technol ITA Sao Jose Dos Campos SP Brazil Blekinge Inst Technol BTH Karlskrona Sweden

ISBN: (纸本)9781510655560

Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers.

关键词： C-Band SAR Feature Extraction PCA CNN Target Classification Machine Learning Sentinel-1

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Accuracy of point scatterer localization across a range of 2D and 3D SAR imaging parameters 28

Accuracy of point scatterer localization across a range of 2...

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conference on algorithms for synthetic aperture radar imagery XXVIII

作者： Pepin, Matthew P. USAF Albuquerque NM 87108 USA

ISBN: (数字)9781510642942

ISBN: (纸本)9781510642942

synthetic aperture radar (SAR) point scatterers are exploited in SAR imaging to detect known locations and objects, and to estimate and identify their specific parameters. The absolute, relative, and differential location accuracy provides the basis for the performance of these functions. Absolute localization can fix the location of an image. Relative localization can estimate object dimensions for identification. And the (time) differential can identify sublimation (sinking) or vibration. Major variations in point scatterers stem from differences in imaging techniques and the geometric (flight) imaging scenario. This paper looks at the gamut of imaging scenarios and the accuracy of parameters estimated from them. The basic location of a point scatterer in an image depends both on the image resolution fixed by the imaging scenario parameters and the sharpness of the point scatterer (impulse response or point spread function). In some cases sub-pixel resolution accuracy is achievable via low-rank image scatterer localization algorithms. Achievable accuracy of both of these is bounded by the imaging scenario and the uniformity of this accuracy across an image which is governed by the imaging technique. Performance varies with waveform parameters, chirp rate, image size, and imaging range. Additionally, performance varies with the different sampling rates that can be used to attain the same image resolution. The performance of scatterer localization techniques across imaging scenarios and example uses of point scatterer localization are presented.

关键词： 3D synthetic aperture radar 3D Localization Convolution Imaging Correlation Imaging Impulse Response Point Spread Function 3D complex damped exponential

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