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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91-100 订阅

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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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Leveraging synthetic imagery to train deep learning algorithms for the detection of objects of interest in radiant energy imagery 28

Leveraging synthetic imagery to train deep learning algorith...

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conference on algorithms for synthetic aperture radar imagery XXVIII

作者： Gonzalez, Megan E. Catlett, Amanda Jabour, Joseph E. Patel, Reena R. Price, Stanton US Army Engineer & Res Dev Ctr 3909 Halls Ferry Rd Vicksburg MS 39180 USA

ISBN: (数字)9781510642942

ISBN: (纸本)9781510642942

Radiant energy object detection deep learning algorithms require large training sets with site-specific images, often from locations that are difficult to access, while also remaining diverse enough to encourage a robust model. Of particular interest is the detection of buried and partially buried objects which have a widely varying behavior profile dependent on factors such as depth, soil composition, time of day, moisture level, target composition, etc. The variety associated with these variables increases the difficulty of acquiring an adequately diverse data set. synthetic imagery offers a potential solution to limited accessibility to data as images can be created on demand with diversity limited only by the parameters of the simulation. The goal of this study is to create custom models using SSD (Single Shot MultiBox Detector), YOLOv3 (You Only Look Once), and Faster R-CNN (Region-based Convolutional Neural Networks) to detect buried objects in real images by leveraging synthetic radiant energy imagery. Custom training is done on a synthetic data set (made in-house) using pre-trained models from Tensorflow's model zoo and ImageAI's YOLOv3 pre-trained model. Model training leverages high performance computing (HPC) resources and utilizes GPU to optimize training speed. Proof-of-concept models for SSD, YOLOv3, and Faster R-CNN have been trained on preliminary synthetic imagery and analyzed. Preliminary results for these models will be discussed.

关键词： Automated Target Recognition High Performance Computing Machine Learning synthetic Infrared imagery Tensorflow 2

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Remote sensing of polar ice: combining synthetic aperture radar and machine learning for operational navigability 28

Remote sensing of polar ice: combining synthetic aperture ra...

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conference on algorithms for synthetic aperture radar imagery XXVIII

作者： Reinisch, Elena C. Ren, Christopher X. Roberts, Andrew Wilson, Cathy Eriksson, Patrick B. Ziemann, Amanda Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA Los Alamos Natl Lab Earth & Environm Sci Div Los Alamos NM 87545 USA Finnish Meteorol Inst PB 503 FI-00101 Helsinki Finland

ISBN: (数字)9781510642942

ISBN: (纸本)9781510642942

Global climate warming is rapidly reducing Arctic sea ice volume and extent. The associated perennial sea ice loss has economic and global security implications associated with Arctic Ocean navigability, since sea ice cover dictates whether an Arctic route is open to shipping. Thus, understanding changes in sea ice thickness, concentration and drift is essential for operation planning and routing. However, changes in sea ice cover on scales up to a few days and kilometers are challenging to detect and forecast;current sea ice models may not capture quickly-changing conditions on short timescales needed for navigation. Assimilating these predictive models requires frequent, high-resolution morphological information about the pack, which is operationally difficult. We suggest an approach to mitigate this challenge by using machine learning (ML) to interpret satellite-based synthetic aperture radar (SAR) imagery. In this study, we derive ML models for the analysis of SAR data to improve short-term local sea ice monitoring at high spatial resolutions, enabling more accurate analysis of Arctic navigability. We develop an algorithm/classifier that can analyze Sentinel-1 SAR imagery with the potential to inform operational sea ice forecasting models. We focus on detecting two sea ice features of interest to Arctic navigability: ridges and leads (fractures in the ice shelf). These can be considered local extremes in terms of ice thickness, a crucial parameter for navigation. We build models to detect these ice features using machine learning techniques. Both our ridge and lead detection models perform as well as, if not better than, state-of-the-art methods. These models demonstrate Sentinel-1's ability to capture sea ice conditions, suggesting the potential for Sentinel-1 global coverage imagery to inform sea ice forecasting models.

关键词： synthetic aperture radar machine learning sea ice ridge detection lead detection

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Image Fusion and Quantum Machine Learning for Remote Sensing Applications

Image Fusion and Quantum Machine Learning for Remote Sensing...

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International conference on Information, Intelligence, Systems and Applications (IISA)

作者： Leslie Miller Glen Uehara Andreas Spanias SenSIP Center ECEE Arizona State University Tempe USA

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The integration of synthetic aperture radar (SAR) and optical imagery through image fusion techniques is important in refining the accuracy and interpretability of images in remote sensing applications. The current array of available image fusion methods presents a complex landscape, each with its own set of advantages and limitations. This survey paper describes existing image fusion methods, with a primary focus on SAR and optical image classification. This will provide valuable insights for enhanced decision-making in remote sensing applications. In addition to examining image fusion methods, we also study various machine learning algorithms that can be used to classify the data. Although the emphasis is on SAR and optical image fusion, the study will also include a broader discussion of other applications, of image fusion methods across various domains.

关键词： Optical filters Visualization Bibliographies Taxonomy Optical imaging Adaptive optics Optical sensors Image fusion synthetic aperture radar Remote sensing

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GCSAR: Small-Object-Enhanced Detection Algorithm Based on Gradient Aggregation and Channel Enhancement for SAR Ship Detection

GCSAR: Small-Object-Enhanced Detection Algorithm Based on Gr...

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Artificial Intelligence Technology (ACAIT), Asian conference on

作者： Kefu Lin Yujuan Han School of Computer Science Shanghai Maritime University Shanghai China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

High-resolution synthetic aperture radar (SAR) utilizes pulse compression technology to accurately resolve the distance between targets and enable imaging,the application of detection algorithm in ship identification task is promoted. However, the current CNN architecture does not perform well for tasks such as low-resolution images and small objects. The features of small targets may be lost during the training process, is one of the factors that makes ship detection missions perform poorly in SAR *** study presents GCSAR, an enhanced detection algorithm tailored for detecting small objects in SAR imagery, focusing specifically on maritime ship detection. The model improves upon the YOLOv8 architecture by incorporating SPDconv in the backbone and Gradient-Aggregation-and-Channel-Enhancement-Module (GACEM) in the neck, allowing for better detection performance in SAR images dataset containing sea vessels images.

关键词： Training Image resolution Object detection Network architecture Feature extraction radar polarimetry Neck Marine vehicles Detection algorithms synthetic aperture radar

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

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

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Communication Technology and Information Technology (ICCTIT), International conference on

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

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

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 $\mathbf{F} 1$ score of 89%, an average accuracy mean Average Precision (mAP) of $\mathbf{9 1 \%}$, 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.

关键词： Accuracy Target recognition Predictive models Feature extraction Prediction algorithms Real-time systems radar polarimetry Marine vehicles synthetic aperture radar Meteorology

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Leveraging Generative Deep Learning Models for Enhanced Change Detection in Heterogeneous Remote Sensing Data

Leveraging Generative Deep Learning Models for Enhanced Chan...

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International conference on Information Fusion

作者： Moslem Ouled Sghaier Melita Hadzagic Jun Ye Yu Sofia Shton Elisa Shahbazian OODA Technologies Inc. Montreal Quebec Canada

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

In this paper, we introduce an innovative approach for Change Detection (CD) in heterogeneous (multimodal) multi-temporal remote sensing (RS) images employing deep features comparison through the utilization of two advanced deep learning models: Generative Adversarial Networks (GANs) and autoencoders. First, Deep Convolutional GANs are implemented to convert multimodal image(s) into synthetic image(s) of the same modality. Subsequently, autoencoders are trained and employed to extract compressed representations of both initial images. Finally, a change map is obtained by combining/fusing the original image with its corresponding generated change-free image resulting from the difference between the two learned compressed representations. Our proposed CD technique can accommodate CD algorithms for RS images expressing any type of change. Experimental evaluations on very high-resolution optical and synthetic aperture radar (SAR) imagery validate the enhanced performance of the proposed method compared to existing state-of-the-art CD techniques in handling heterogeneous RS data.

关键词： Training Deep learning Analytical models Adaptation models Image coding Generative adversarial networks Feature extraction Satellite images Remote sensing synthetic aperture radar

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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

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.

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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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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Convolutional Neural Network Combined with Transfer Learning for Damage Assessment with Satellite imagery 2

Convolutional Neural Network Combined with Transfer Learning...

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2022 2nd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2022

作者： Hu, Sijia School of Mechanical Electrical & Information Engineering Shandong University Weihai Shandong Weihai264209 China

ISBN: (纸本)9783800760268

Damage assessment is imperative for humanitarian and post-event reconstruction as well as crucial to the relief helpers for resources distribution after a hurricane. Compared to visual inspections and optical and synthetic aperture radar imagery, the article combined satellite images with deep learning algorithms to automatically and efficiently distinguish the post-event damage. In this study, a self-designed CNN model was constructed initially. To enhance the performance, transfer learning based on different backbone networks was proposed. The backbone networks include VGG16, Res-Net50, InceptionV3, and MobileNetv2. For the same backbone network, on one hand, the paper freezes different numbers of layers for comparison. On the other hand, on the premise that the number of freezing layers is the same, the paper studies the use of the optimizer (Adam and RMSprop) and the top layer, namely the fully connected layer and global average pooling layer. For different backbone networks, under the condition that the number of frozen network layers, optimizer, top layer, and other parameters are the same, the performance of different models is compared and studied in this paper. To assess the classification performance, this study chose accuracy and the area under the receiver operating characteristic curve (AUC) as the main metrics. It can be concluded that using global average layers, transfer learning widely outperformed fully connected layers. ResNet50 combined with Adam optimizer could achieve an accuracy of 99.6% on the balanced test set and 99.3% on the unbalanced test set, which is the best performance of the proposed algorithms. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Network layers

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