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

共 870 条记录，以下是1-10 订阅

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A Sidelobe-Aware Semi-Deformable Convolutional Ship Detection Network for synthetic aperture radar imagery 7th

A Sidelobe-Aware Semi-Deformable Convolutional Ship Detectio...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Luo, Hao Lin, Xianming Xiamen Univ Minist Educ Key Lab Multimedia Trusted Percept & Efficient Co Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

This paper focus on SAR ship detection, which has been widely used in tasks such as marine traffic, fisheries management, battlefield posture assessment and military target reconnaissance. One popular solution is to utilise deep learning algorithms in conjunction with SAR image object detection. However, due to the unique imaging characteristics of SAR, existing solutions usually do not distinguish well between ships and interference targets in complex backgrounds. In this paper, we address this problem by proposing a sidelobe-aware semi-deformable convolution that takes full advantage of the combination of both standard and deformable convolution to learn high-quality ship features without significantly increasing the computational complexity. Specifically, it makes the feature extraction location more closely fit to the ship shape, strengthens the extraction capability of the region of the ship target itself and the sidelobe features, while reducing the extraction of background information. The channel attention mechanism is then proposed to enhance the ship local detail information extraction. Experiments on two widely used datasets show that the proposed method outperforms the state-of-the-art methods, which is effective and efficient to improve SAR ship detection.

关键词： Deformable Convolution synthetic aperture radar (SAR) imagery Ship Detection Remote Sensing Image Processing

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A Novel Technique to Remove Noise from SAR Images by Equalizing Neighborhood Surrounding Values and Pixel Smoothing 2

A Novel Technique to Remove Noise from SAR Images by Equaliz...

引用

2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. CSE National Institute of Technology Uttarakhand India Dept. CSE National Institute of Technology Patna India

ISBN: (纸本)9798331530389

Automatic Target Recognition (ATR) plays a crucial role in military operations to locate actual targets and response immediately. Target recognition is easier in Standard Operating Conditions (SOC) while it becomes very complex in Extended Operating Conditions (EOC) due to environmental noise, large depression variations, partial occlusion and limited training samples. Deep Learning (DL) has a wide applications in synthetic aperture radar (SAR) imagery. Interpretation of SAR images to recognize actual targets is a major challenge. Researchers from across the world has given different deep learning models for ATR and it is found that most of DL algorithms performs poor in EOC's. We have increased samples of the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset by adding partially occluded targets, different levels of noise and multiple objects in samples of the MSTAR. This paper presents an Object Localization & Deep Learning-based model for ATR. Our model contains a noise removal phase using spatial filtering and feature extraction and also, classification using enhanced VGG-19. The performance of the proposed model is satisfactory. © 2025 IEEE.

关键词： radar imaging

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Harnessing Satellite imagery for Effective Flood Detection using Convolution Neural Network 3

Harnessing Satellite Imagery for Effective Flood Detection u...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ambarkar, Bhakti Singh, Aman Gupta, Piyush Dhule, Chetankumar Agrawal, Rahul Morris, Nekita Chavhan G H Raisoni College Of Engineering Department Of DIC Nagpur India Department of Data Science G H Raisoni College of Engineering Nagpur India Department of DIC G H Raisoni college of Engineering Nagpur India

ISBN: (纸本)9798331527549

Earth Observation has become essential for monitoring geosciences and human activity. With the increasing availability of data, Artificial Intelligence (AI) algorithms have delivered significant advancements in remote sensing. However, weather-related disasters like hurricanes and floods, often accompanied by dense cloud cover, limit the effectiveness of optical imaging. synthetic aperture radar (SAR), which penetrates clouds, offers a robust alternative. This study presents a novel dataset of co-registered optical and SAR image time series for flood event detection. By integrating spatial and temporal information using a ResNet-GRU architecture, we overcome the challenges posed by limited labeled SAR data. Our method achieves state-of-the-art accuracy, demonstrating resilience in detecting floods under adverse conditions. Performance metrics indicate substantial improvements in disaster response capabilities, emphasizing the practical utility of SAR in flood monitoring. This work provides critical insights for researchers and practitioners in leveraging SAR and optical data for disaster management. © 2025 IEEE.

关键词： radar imaging

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High Resolution Millimeter Wave Imaging Based on FMCW radar Systems at W-Band

High Resolution Millimeter Wave Imaging Based on FMCW Radar ...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Shahrokh Hamidi MR Nezhad-Ahmadi Department of Electrical and Computer Engineering University of Waterloo Canada MMSENSE Technologies Inc. Waterloo Canada

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In this paper, we present a unique 2D high resolution, compact, low-cost, lightweight, and highly accurate millimeter-wave imagery system capable of operating in all weather conditions. We explain different aspects of the imagery system in detail and describe the process of millimeter-wave image formation thoroughly. To create the array, we utilize the synthetic aperture radar (SAR) concept. The imagery system presented in this paper, can strongly compete with the Lidar systems as the resolution limit is at the same level. Furthermore, in contrast to the Lidar systems, our imagery system can operate in heavy rain as well as dense fog and produce high quality images. Finally, we utilize our wide-band custom-made Frequency Modulated Continuous Wave (FMCW) radar, which operates at W-band with 33 GHz bandwidth, for data collection and present the results.

关键词： Image resolution Laser radar Frequency modulation Imaging Signal processing algorithms Millimeter wave radar radar imaging Millimeter wave communication synthetic aperture radar Signal resolution

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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...

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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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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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A Novel Technique to Remove Noise from SAR Images by Equalizing Neighborhood Surrounding Values and Pixel Smoothing

A Novel Technique to Remove Noise from SAR Images by Equaliz...

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Computational Intelligence, Communication Technology and Networking (CICTN), International conference on

作者： Baldivya Mitra Maroti Deshmukh Abhimanyu Kumar Dept. CSE National Institute of Technology Uttarakhand India Dept. CSE National Institute of Technology Patna India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Automatic Target Recognition (ATR) plays a crucial role in military operations to locate actual targets and response immediately. Target recognition is easier in Standard Operating Conditions (SOC) while it becomes very complex in Extended Operating Conditions (EOC) due to environmental noise, large depression variations, partial occlusion and limited training samples. Deep Learning (DL) has a wide applications in synthetic aperture radar (SAR) imagery. Interpretation of SAR images to recognize actual targets is a major challenge. Researchers from across the world has given different deep learning models for ATR and it is found that most of DL algorithms performs poor in EOC’s. We have increased samples of the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset by adding partially occluded targets, different levels of noise and multiple objects in samples of the MSTAR. This paper presents an Object Localization & Deep Learning-based model for ATR. Our model contains a noise removal phase using spatial filtering and feature extraction and also, classification using enhanced VGG-19. The performance of the proposed model is satisfactory.

关键词： Deep learning Training Image recognition Target recognition Computational modeling Working environment noise Noise radar polarimetry synthetic aperture radar Standards

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Harnessing Satellite imagery for Effective Flood Detection Using Convolution Neural Network

Harnessing Satellite Imagery for Effective Flood Detection U...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Bhakti Ambarkar Aman Singh Piyush Gupta Chetankumar Dhule Rahul Agrawal Nekita Chavhan Morris Department Of DIC G H Raisoni College Of Engineering Nagpur India Department of Data Science G H Raisoni College of Engineering Nagpur India Head of Department of DIC G H Raisoni college of Engineering Nagpur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

关键词： Disasters Time series analysis Optical computing Optical imaging Adaptive optics radar polarimetry Floods Optical sensors synthetic aperture radar Monitoring

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Signature Predictions of Surface Targets Undergoing Braking Maneuvers in Squinted Spotlight synthetic aperture radar imagery 23

Signature Predictions of Surface Targets Undergoing Braking ...

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conference on algorithms for synthetic aperture radar imagery xxiii

作者： Garren, David A. Naval Postgrad Sch Monterey CA 93943 USA

ISBN: (纸本)9781510600843

This paper investigates methodologies for predicting the smear signatures in squinted spotlight synthetic aperture radar (SAR) imagery collections due to surface targets that are undergoing braking maneuvers. Previous analysis considered the case of broadside collection geometries. Analytic computation of a power series expansion is used to compute a generic expression for the down-range and cross-range components of the predicted mover signature. In addition, recent analysis presents capabilities for predicting the full signature shape, including the smear width and interference effects. The current investigations focuses on the effects of squinted collection geometries upon braking targets signatures.

关键词： synthetic aperture radar moving targets radar signatures radar imagery

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Convolutional Neural Networks for synthetic aperture radar Classification 23

Convolutional Neural Networks for Synthetic Aperture Radar C...

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conference on algorithms for synthetic aperture radar imagery xxiii

作者： Profeta, Andrew Rodriguez, Andres Clouse, H. Scott Wright State Univ Dayton OH 45435 USA Intel Corp Hillsboro OR 97124 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510600843

For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

关键词： synthetic aperture radar radar Neural networks Convolutional neural networks Algorithm development Machine learning Automatic target recognition Network architectures Detection and tracking algorithms Speckle

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