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

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Intelligent Interpretation Technology of remote sensing Images based on Deep Learning Algorithms

Intelligent Interpretation Technology of Remote Sensing Imag...

引用

2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sui, Xuelian China Resource Satellite Application Center Beijing China

ISBN: (纸本)9798350360660

In recent years, high-resolution remote sensing images have been increasingly used in the field of geoscience. Therefore, how to interpret high-resolution remote sensing images with high quality is an urgent problem to be solved. This paper takes high-resolution remote sensing images as the research object, uses deep convolutional neural network (CNN) technology, conducts remote sensing image segmentation and feature extraction based on deep convolutional neural network, establishes a high-precision remote sensing image interpretation model, and improves the accuracy and computing speed of intelligent recognition of remote sensing images. This paper uses residual network (ResNet) as a learning sample to compare its performance with the classical algorithm on the same set of samples. Preliminary studies of this project have shown that the accuracy of convolutional neural networks in image analysis can reach 97.1%, and it shows obvious superiority at multiple spatial scales, and can effectively improve the efficiency of image interpretation. © 2024 IEEE.

关键词： Convolutional neural networks

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Maize Leaf Biomass Retrieval at Multi-growing Stage Using UAV Multispectral Images Based on 3D Radiative Transfer Process-guided Machine Learning 12

Maize Leaf Biomass Retrieval at Multi-growing Stage Using UA...

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12th International conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Zhao, Dan Yang, Hao Yang, Guijun Xu, Xingang Xu, Bo Minist Agr & Rural Affairs Key Lab Quantitat Remote Sensing Agr Informat Technol Res Ctr Beijing Peoples R China Beijing Acad Agr & Forestry Sci Beijing Peoples R China

ISBN: (纸本)9798350380613;9798350380606

high-precision estimation of above-ground biomass (AGB) is very crucial for breeding field. Optical variables (e.g. vegetation index (vi)) have widely used in monitoring AGB. In this study, we used a stem-leaf separation strategy to estimate biomass. The combined use of multispectral and deep learning techniques (e.g., convolutional neural network (CNN) and transfer learning (TL)) estimate leaf biomass (LGB). Then, an allometric growth model was used to estimate stem biomass (SGB). We used three-dimensional radiative transfer (3D RTM) - LESS model to simulate a universality and realistic multispectral dataset (n = 44880). We designed a CNN architecture that can extract multi-layer feature of CNNs. This study combined 3D RTM dataset, CNN and TL techniques to estimate maize LGB of multiple growth stage. The results showed our method had the best performance in LGB estimation at multiple growth stage. The result showed that using the allometric growth model to estimate SGB achieved an R-2 of 0.83 and an RMSE of 67.5 g/m(2), improving the prediction accuracy of SGB. This study utilized the advantage of 3D RTM, CNN, TL, and allometric model which can monitor maize AGB more accurately for breeding filed.

关键词： maize above-ground biomass stem-leaf separation convolutional neural network transfer learning UAV multispectral

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Vegetation Classification of UAV Multispectral remote sensing Images Based on Deep Learning 3

Vegetation Classification of UAV Multispectral Remote Sensin...

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3rd International conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Xue, Jiaming Sun, Shanlin Zhao, Haimeng Chen, Wei School of Electronic and Information Engineering Guangxi Normal University Guilin541006 China Guangxi Colleges and Universities Guilin University of Aerospace Technology Key Laboratory of Unmanned Aerial Vehicle Remote Sensing Guilin541004 China College of Geoscience and Surveying Engineering China University of Mining & Technology Beijing100083 China

ISBN: (纸本)9798350331578

With the aim of providing a reliable prediction model for vegetation detection and ground classification, a multispectral dataset was produced for semantic segmentation, which utilizes multispectral UAV images and is based on a combination of support vector machines and manual annotation. Also, a 3D-UNet model is proposed on which the dataset is trained and experiments show that the model has achieved 89.9 % prediction for the validation set. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Intelligent Interpretation Technology of remote sensing Images based on Deep Learning Algorithms

Intelligent Interpretation Technology of Remote Sensing Imag...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International conference on

作者： Xuelian Sui China Resource Satellite Application Center Beijing China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

关键词： Image segmentation Image recognition Accuracy Computational modeling Face recognition Feature extraction Classification algorithms Convolutional neural networks remote sensing Residual neural networks

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Intelligent Edge-Based System for Driver Drowsiness Monitoring in Mobile Crowd-sourcing Environments 2

Intelligent Edge-Based System for Driver Drowsiness Monitori...

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2nd International conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Neels Ponkumar, D. David Ramesh, S. Ahamed, A. Niyas Baskar, S. Ashok, Chitrada Charan, M. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing College of Engineering and Technology SRMIST Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India Thamirabharani Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Civil Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350366570

Using CNN and GAN improves road safety, says study. Modern machine learning techniques for mobile crowdsourcing may identify driver fatigue early. Our convolutional neural network (CNN) model detects drivers' fatigue using facial expressions, head movements, and other visual clues. Mobile devices and high-resolution cameras make the system straightforward to install in automobiles. GAN (Generative Advisal Network) handles unequal and limited training data. Creating realistic, synthetic weary driver photos improves the dataset and helps CNN learn from different driving scenarios and face features. Our edge-based computing distinguishes it. In mobile crowdsourcing with unpredictable internet connections, processing data locally on users' smartphones minimizes latency and bandwidth. The edge-based solution processes sensitive data locally on the device rather than sending it to servers, increasing user privacy. Extensive testing showed our system diagnosed driver weariness better than others. GAN-generated data increased model performance in under-represented training datasets. The study shows how to merge CNN and GAN in edge computing with 98.4% accuracy and driver fatigue monitoring. We found important implications for mobile crowdsourcing and intelligent transportation system development. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluating Data Speed for 5G Mobile Technologies using Queuing Models 4

Evaluating Data Speed for 5G Mobile Technologies using Queui...

引用

4th IEEE Asia-Pacific conference on geoscience, Electronics and remote sensing Technology (AGERS)

作者： Baryun, Abdussalam Nuri Ben Hamed, Khaled Univ Tripoli Mobile Comp Dept Fac Informat Technol Tripoli Libya

ISBN: (纸本)9781665428071

high speed communication and data movement is the key for mobile technologies and systems evolutions, which requires broadband communications and intelligent networking. The 5G mobile infrastructure enables development of many mobile technologies to fully use new technologies such as AI, mobile edge computing, M2M and data analytics. 5G/IMT-2020 will provide new applications and services for both developed and developed countries. Applications such as smart transport systems, e-health, education, smart grid, agriculture and disaster relief. In this paper we simulate and evaluate the mobile system related to its mobile application by using suitable queuing models to provide best performance based on its QoS.

关键词： 5G Communication Technologies Mobile computing ITS IoT M2M D2D Queuing Models

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IGARSS 2021 virtual Symposium: Impressions of the First Days [conference Reports]

IGARSS 2021 Virtual Symposium: Impressions of the First Days...

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作者： Moreira, Alberto Judge, Jasmeet Bovolo, Francesca Plaza, Antonio

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Development of high performance computing Tools for Estimation of high-Resolution Surface Energy Balance Products Using sUAS Information 6

Development of High Performance Computing Tools for Estimati...

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conference on Autonomous Air and Ground sensing Systems for Agricultural Optimization and Phenotyping vi

作者： Nassar, Ayman Torres, Alfonso Merwade, Venkatesh Dey, Sayan Zhao, Lan Kim, I. Luk Kustas, William P. Nieto, Hector Hipps, Lawrence Gao, Rui Alfieri, Joseph Prueger, John Alsina, Maria Mar McKee, Lynn Coopmans, Calvin Sanchez, Luis Dokoozlian, Nick Ortiz, Nicolas Bambach Mcelrone, Andrew J. Utah State Univ Dept Civil & Environm Engn Logan UT 84322 USA Utah State Univ Utah Water Res Lab Logan UT 84322 USA Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA ARS USDA Hydrol & Remote Sensing Lab Beltsville MD USA Complutum Tecnol Informac Geog Madrid Spain Utah State Univ Plants Soils & Climate Dept Logan UT 84322 USA ARS USDA Natl Lab Agr & Environm Ames IA USA E&J Gallo Winery Viticulture Res Modesto CA USA Utah State Univ Elect Engn Logan UT 84322 USA Univ Calif Davis Davis CA 95616 USA

ISBN: (数字)9781510643321

ISBN: (纸本)9781510643321

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEXproject, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://***/ to be reproducible and replicable.

关键词： surface energy balance cyberinfrastructure remote sensing sUAS myGeoHub HPC Python TSEB2T FAIR

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Implementing a Scalable and Elastic computing Environment Based on Cloud Containers 21st

Implementing a Scalable and Elastic Computing Environment Ba...

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21st International conference on Computational Science and Its Applications (ICCSA)

作者： Perri, Damiano Simonetti, Marco Tasso, Sergio Ragni, Federico Gervasi, Osvaldo Univ Florence Dept Math & Comp Sci Florence Italy Univ Perugia Dept Math & Comp Sci Perugia Italy Univ Perugia Informat Syst Div Perugia Italy

ISBN: (纸本)9783030866532;9783030866525

In this article we look at the potential of cloud containers and we provide some guidelines for companies and organisations that are starting to look at how to migrate their legacy infrastructure to something modern, reliable and scalable. We propose an architecture that has an excellent relationship between the cost of implementation and the benefits it can bring, based on the "Pilot Light" topology. The services are reconfigured inside small docker containers and the workload is balanced using load balancers that allow horizontal autoscaling techniques to be exploited in the future. By generating additional containers and utilizing the possibilities given by load balancers, companies and network systems experts may model and calibrate infrastructures based on the projected number of users. Containers offer the opportunity to expand the infrastructure and increase processing capacity in a very short time. The proposed approach results in an easily maintainable and fault-tolerant system that could help and simplify the work in particular of small and medium-sized organisations.

关键词： high availability Docker Load balancing Elastic computing Disaster recovery high performance computing Public cloud Private cloud Hybrid cloud

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Parallel implementation of an iterative solver for atmospheric tomography 21

Parallel implementation of an iterative solver for atmospher...

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21st International conference on Computational Science and Its Applications (ICCSA)

作者： Stadler, Bernadett Biasi, Roberto Manetti, Mauro Ramlau, Ronny Johannes Kepler Univ Linz Ind Math Inst Linz Austria Microgate Bolzano Italy

ISBN: (纸本)9781665458436

Due to atmospheric turbulences the image quality of earthbound Extremely Large Telescopes (ELTs) is heavily degraded. Adaptive Optics (AO) is a technique that compensates for these quickly-varying optical aberrations. Many AO systems require the reconstruction of the turbulence profile in the atmosphere, called atmospheric tomography. The standard way of solving this problem is the so called Matrix Vector Multiplication method, i.e., the direct application of a (regularized) generalized inverse of the system operator. However, over the last years the telescope sizes have increased significantly and solving the atmospheric tomography problem in real-time become an ambitious and crucial task. Promising alternatives are iterative methods such as the Finite Element Wavelet Hybrid Algorithm (FEWHA), which is based on wavelets. Due to its efficient matrix-free representation of the underlying operators, the number of floating point operations and memory usage decrease significantly. In this paper, we focus on a parallel implementation of FEWHA on real-time hardware used for the control of large AO systems. We evaluate the computational performance of our parallel algorithm via numerical simulations for ELT-sized test configurations.

关键词： adaptive optics atmospheric tomography high performance computing iterative solvers inverse problems

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