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检索条件"任意字段=Conference on High-Performance Computing in Remote Sensing IV"

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A Multi-Level Parallel Algorithm for Detection of Single Scatterers in SAR Tomography 33

A Multi-Level Parallel Algorithm for Detection of Single Sca...

引用

33rd Euromicro International conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Russo, Massimiliano Nisar, Mehwish Pauciullo, Antonio Imperatore, Pasquale Lapegna, Marco Romano, Diego University of Naples Federico II Dept. of Mathematics and Applications Naples Italy National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (纸本)9798331524937

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth's surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous high-performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring. © 2025 IEEE.

关键词： Light scattering

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Dynamic Loss Decay Based Robust Oriented Object Detection on remote sensing Images with Noisy Labels 27th

Dynamic Loss Decay Based Robust Oriented Object Detection on...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Liu, Guozhang Liu, Ting Yuan, Mengke Pang, Tao Yang, Guangxing Fu, Hao Wang, Tao Liao, Tongkui Piesat Information Technology Beijing China

ISBN: (纸本)9783031781094

The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase "early-learning" and "memorization" learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and computing Intelligence Challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Research on the Application of remote Health Monitoring System Based on Intelligent sensing in Home-Based Elderly Care

Research on the Application of Remote Health Monitoring Syst...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Li, Miao Jiang, Rui Changchun Humanities and Sciences College Faculty of Social Welfare Dept. of Social Welfare Jilin Changchun130117 China Changchun Humanities and Sciences College Office of Academic Research Jilin Changchun130117 China

ISBN: (纸本)9798331504205

This paper designs a real-time stream processing algorithm driven by intelligent sensing to meet the high requirements of real-time and accuracy in home-based elderly care scenarios. The system collects physiological and behavioral data by integrating multimodal sensors (such as heart rate, blood oxygen, motion sensors, etc.), and uses distributed stream processing architecture to realize real-time analysis and anomaly detection of data. Then an improved dynamic threshold adaptive algorithm is proposed, which significantly improves the detection accuracy of health events while reducing the false alarm rate. The performance and reliability of the system are verified based on real health data sets in model simulation. The experimental results show that the designed system achieves an accuracy of 98.5% in abnormal event detection, and the processing delay is controlled within 200 milliseconds, which is more than 30% better than the traditional algorithm. In addition, the system has good scalability and stability in multi-task concurrent scenarios. This remote health monitoring system can effectively improve the health management level of home-based elderly care and provide personalized and efficient health protection for the elderly. © 2025 IEEE.

关键词： Electronic health record

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Classification of Vehicle Classes at Toll Gates Using YOLOv9 and Resnet50

Classification of Vehicle Classes at Toll Gates Using YOLOv9...

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2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Febriyansah, Abdul Aziz Kosala, Gamma School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Indonesia's current toll road payment system uses Automatic Vehicle Classification (AVC) technology, which relies on various sensors such as induction sensors, LiDAR, and radar. This approach incurs high operational costs. For this reason, more efficiency is required. Toll roads are vital infrastructure supporting vehicle mobility in Indonesia, and operational efficiency directly impacts overall performance. The existing AVC technology entails high costs and often faces technical challenges, necessitating a solution that enhances efficiency in terms of operational costs, accuracy, and system maintenance. This research combines the You Only Look Once (YOLO) method for detecting vehicle axles, represented by tires, and Residual Network (ResNet) for classifying vehicle categories based on YOLO's detection results. The dataset comprises 1,196 vehicle images across five distinct classes. The proposed system aims to improve accuracy, cost, and system maintenance efficiency. The findings indicate that the system achieves a classification accuracy of 97%, providing a practical solution that contributes to the advancement of toll gate vehicle classification technology with lower operational costs and higher accuracy. © 2025 IEEE.

关键词： remote sensing

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Lightweight remote sensing Target Detection Algorithm Based on Improved YOLOv5 16th

Lightweight Remote Sensing Target Detection Algorithm Based ...

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16th International conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Wang, Chaomin Dong, Fang Liang, Sheng Yu, Wanxin Wu, Kaixian Yu, Long Wenfeng, Wang School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Jiangxi Province Key Laboratory of Smart Water Conservancy Nanchang Institute of Technology Nanchang330099 China School of Statistics and Data Science Jiangxi University of Finance and Economics Nanchang330013 China

ISBN: (纸本)9789819615346

Aiming at the problems of large memory space occupied by remote sensing image detection models in complex scenes, high computational complexity, and detection speed that is difficult to meet the real-time target detection requirements, a model compression algorithm based on dynamic detection head and channel pruning is proposed. Based on YOLOv5s, the head of the original YOLOv5 is replaced by the DyHead detection head, which improves the detection performance of the model by combining and unifying the three attention mechanisms of scale, space and task. Secondly, the Focaler-IoU loss function is introduced to further improve the predictive regression ability of the model. Finally, the channel pruning algorithm is used to eliminate the redundant parameters in the network to further compress the parameters and computation of the model, and the accuracy of the pruned model is improved by model fine-tuning. The experimental results show that there is a large improvement on the DOTAV1.0 dataset, and the validity is further verified on the RSOD dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Jurassic

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Exploring Segment Anything Foundation Models for Out of Domain Crevasse Drone Image Segmentation 6

Exploring Segment Anything Foundation Models for Out of Doma...

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6th Northern Lights Deep Learning conference, NLDL 2025

作者： Wallace, Steven Durrant, Aiden Harcourt, William D. Hann, Richard Leontidis, Georgios School of Natural and Computing Sciences University of Aberdeen United Kingdom School of Geosciences University of Aberdeen United Kingdom Interdisciplinary Institute University of Aberdeen United Kingdom Department of Engineering Cybernetics Norwegian University of Science and Technology Norway

In this paper, we explore the application of Segment Anything (SAM) foundation models for segmenting crevasses in Uncrewed Aerial Vehicle (UAV) images of glaciers. We evaluate the performance of the SAM and SAM 2 models on ten high-resolution UAV images from Svalbard, Norway. Each SAM model has been evaluated in inference mode without additional fine-tuning. Using both automated and manual prompting methods, we compare the segmentation quantitatively using Dice Score Coefficient (DSC) and Intersection over Union (IoU) metrics. Results show that the SAM 2 Hiera-L model outperforms other variants, achieving average DSC and IoU scores of 0.43 and 0.28 respectively with automated prompting. However, the overall off-the-shelf performance suggests that further improvements are still required to enable glaciologists to examine crevasse patterns and associated physical processes (e.g. iceberg calving), indicating the need for further fine-tuning to address domain shift challenges. Our results highlight the potential of segmentation foundation models for specialised remote sensing applications while also identifying limitations in applying them to high-resolution UAV images, as well as ways to enhance further model performance on out-of-domain glacier imagery, such as few-shot and weakly supervised learning techniques. © NLDL *** rights reserved.

关键词： Image segmentation

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Research of Simulation on Real-Time Transmission and Processing of high Orbit remote sensing Satellite Data Based on Virtualization Technology

Research of Simulation on Real-Time Transmission and Process...

引用

IEEE International conference on Big Data Security on Cloud (BigDataSecurity), high performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Yuming Zhou Feng Xiong Yuanyuan Liu Xiaobing Yin Beijing Institute of Remote Sensing Information Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Efficient collaborative operation of high orbit remote sensing satellite data transmission and processing equipment is an effective guarantee for the efficient production, quality and cost of remote sensing data image products. In order to improve the operational efficiency of remote sensing data transmission and processing system equipment, optimize equipment configuration schemes, save equipment cost and accelerate data processing efficiency, this paper builds a network transmission simulation model based on virtualization technology in response to the high real-time and large data volume characteristics of high orbit satellite observation. Based on Arena software, simulation research was conducted on the operational efficiency of system processing equipment. Combined with the specific engineering example of Gaofen-4 satellite, model parameter setting and simulation are carried out, and the operational efficiency under different equipment configuration schemes are analyzed. The study provides a reference for decision-making of the actual operation of high orbit remote sensing satellite data transmission and processing.

关键词： Satellites Systems operation Data processing Orbits Real-time systems Data models Software Data communication Virtualization remote sensing

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A Multi-Level Parallel Algorithm for Detection of Single Scatterers in SAR Tomography

A Multi-Level Parallel Algorithm for Detection of Single Sca...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Massimiliano Russo Mehwish Nisar Antonio Pauciullo Pasquale Imperatore Marco Lapegna Diego Romano Dept. of Mathematics and Applications University of Naples Federico II Naples Italy Institute for the Electromagnetic Sensing of the Environment (IREA) National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth’s surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous high-performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring.

关键词： high performance computing Scalability Graphics processing units Tomography performance analysis Parallel algorithms Synthetic aperture radar Surface treatment remote sensing Optimization

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Generation of Synthetic Dataset for Electronic Health Record (EHR) and Medical Images

Generation of Synthetic Dataset for Electronic Health Record...

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International conference on ent computing and Control Systems (ICICCS)

作者： K Vipul Arya Sathya D School of Computer Science Engineering (SOCSE) RV University Bengaluru

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Machine learning models require large quantities of high-quality data for training. In applications such as rare disease diagnosis, remote sensing, and agriculture, data unavailability is a cutting-edge constraint leading to sub-optimal model performance. In this paper, we explore the application of data augmentation by deep learning-based Generative Adversarial Networks (GANs) and Diffusion models for generating synthetic data sets for Electronic Health Records (EHRs) and medical images. Due to medical data privacy and limited access for high-resolution medical data, synthetic data generation is one potential direction towards training effective machine learning models. Our aim is to compare the synthetic medical data generation performance of the GAN and Diffusion model and choose the most effective solution. Utilizing synthetic data, our aim is to enhance model performance without compromising data privacy and enhancing training data availability for medical research. The proposed approach is extensible to broad ranges of applications in non-medical domains such as security, remote sensing, and anomaly detection. Experiments demonstrate that the use of synthetic datasets generated on the basis of GAN enhances the accuracy of the model and thus is an efficient approach for machine learning-based data augmentation.

关键词： Training Electric potential Generative adversarial networks Diffusion models Data augmentation Data models Electronic medical records Medical diagnostic imaging remote sensing Synthetic data

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Modification of YOLOv4-Tiny-3L Architecture to Improve the Accuracy of Airport Object Detection

Modification of YOLOv4-Tiny-3L Architecture to Improve the A...

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International conference on New Trends in computing Sciences (ICTCS)

作者： Ahmad Fauzan Novy Nur R.A Mokobombang Department of Informatics Hasanuddin University Makassar Indonesia

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Accurate object detection in remote sensing is crucial for air traffic surveillance, border security, and disaster response. However, detecting aircraft and other objects in aerial imagery remains challenging due to small object sizes, complex backgrounds, and occlusion. YOLOv4-Tiny-3L, a lightweight object detection model, suffers from limited small object detection capabilities and suboptimal feature extraction, leading to reduced performance in high-precision tasks. This study proposes an enhanced YOLOv4-Tiny-3L architecture by integrating Cross-Stage Partial (CSP) connections and an additional multi-scale YOLO head to improve feature extraction and detection accuracy, particularly for small and densely packed objects. The modifications significantly enhance multi-scale feature representation while maintaining low computational complexity. Experimental evaluations on aircraft and remote sensing datasets demonstrate substantial performance gains, with mAP(50) improving from 0.91,77 to 0.93,23 on the Aircraft Classification dataset and from 0.97,98 to 0.98,41 on the RSOD dataset. Compared to state-of-the-art models such as YOLOv4 and Faster R-CNN, the proposed approach achieves superior accuracy while preserving efficiency, making it highly suitable for real-time applications in airport security and aerial monitoring. These findings highlight the effectiveness of CSP-based architectural enhancements in lightweight object detection models, offering a more efficient and precise alternative for remote sensing applications.

关键词： YOLO Accuracy Atmospheric modeling Object detection Computer architecture Airports Feature extraction Security Aircraft remote sensing

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