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Full-Range Fusion Network with Local-Global Attention for Change Detection in remote sensing Images 1

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20th International conference on Intelligent computing (ICIC)

作者： Niu, Shuting Zhang, Yingxue Si, Zhanjun Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China

ISBN: (数字)9789819756001

ISBN: (纸本)9789819755998;9789819756001

remote sensing image change detection (CD) is an important technology used to monitor changes in surface features and objects over time, widely applied in fields such as land use planning, environmental monitoring, and disaster management. Although traditional change detection methods are effective in certain scenarios, they are often limited by high sensitivity to noise and computational complexity, making them unsuitable for the rapidly evolving demands of big data. In response, this paper proposes an efficient local-global context fusion network (LGCF-Net) based on a Siamese architecture, aimed at enhancing the accuracy and efficiency of change detection in remote sensing images. LGCF-Net incorporates efficient local-global context aggregator (ELGCA) module and cross fusion attention module (CFAM) to effectively improve the performance of change detection. The proposed method is evaluated on the SYSU-CD dataset, achieving accuracy and recall of 93.21% and 95.39%, respectively, which confirms the effectiveness of the proposed method.

关键词： remote sensing image Change detection Siamese architecture Efficient Local-global context aggregator module Cross fusion attention module

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A new tool for supervised classification of satellite images available on web servers: Google Maps as a case of study 6

A new tool for supervised classification of satellite images...

引用

conference on high-performance computing in geoscience and remote sensing VI

作者： Garcia-Flores, Agustin Paz-Gallardo, Abel Plaza, Antonio Li, Jun Univ Extremadura Hyperspectral Comp Lab Caceres Spain CIEMAT Trujillo Spain Sun Yat Sen Univ Sch Geog & Planning Ctr Integrated Geog Informat Anal Guangdong Prov Key Lab Urbanizat & Geosimulat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781510604186;9781510604193

This paper describes a new web platform dedicated to the classification of satellite images called Hypergim. The current implementation of this platform enables users to perform classification of satellite images from any part of the world thanks to the worldwide maps provided by Google Maps. To perform this classification, Hypergim uses unsupervised algorithms like Isodata and K-means. Here, we present an extension of the original platform in which we adapt Hypergim in order to use supervised algorithms to improve the classification results. This involves a significant modification of the user interface, providing the user with a way to obtain samples of classes present in the images to use in the training phase of the classification process. Another main goal of this development is to improve the runtime of the image classification process. To achieve this goal, we use a parallel implementation of the Random Forest classification algorithm. This implementation is a modification of the well-known CURFIL software package. The use of this type of algorithms to perform image classification is widespread today thanks to its precision and ease of training. The actual implementation of Random Forest was developed using CUDA platform, which enables us to exploit the potential of several models of NVIDIA graphics processing units using them to execute general purpose computing tasks as image classification algorithms. As well as CUDA, we use other parallel libraries as Intel Boost, taking advantage of the multithreading capabilities of modern CPUs. To ensure the best possible results, the platform is deployed in a cluster of commodity graphics processing units (GPUs), so that multiple users can use the tool in a concurrent way. The experimental results indicate that this new algorithm widely outperform the previous unsupervised algorithms implemented in Hypergim, both in runtime as well as precision of the actual classification of the images.

关键词： remote sensing data processing satellite/airborne image classification web-based system Random Forest GPGPU

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Optimization for viewshed analysis on GPU

Optimization for viewshed analysis on GPU

引用

International conference on Geoinformatics

作者： Gao, Yong Yu, Hao Liu, Yu Liu, Yuehu Liu, Mingchao Zhao, Yong Institute of Remote Sensing and Geographic Information System Peking University Beijing China

ISBN: (纸本)9781612848488

Different algorithms have been raised for viewshed analysis and measures were taken to get the compromise between performance and accuracy. The most accurate and standard algorithm is still the basic interpolation method, though its time cost is high. However, the development of Graphic Processing Unit (GPU) enables us to acquire high performance with normal PC, especially when the Compute Unified Device Architecture (CUDA) is put forward by NVIDIA for general purpose computing. In this paper, we will analyze the feasibility to map the basic interpolation method into GPU application and give our approach to achieve this goal. Further, we will introduce two critical measures in this approach: one is how to assign the data into different memory spaces on GPU according to their different access characteristics;the other is how to regularize the computing instructions and minimize branch parts in the procedure. At most, nearly 70 times speedup is reached in the experiment compared with the basic interpolation method on CPU. © 2011 IEEE.

关键词： Graphics processing unit

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Radar & Additive Manufacturing Technologies: the Future of Internet of Things (IoT)

Radar & Additive Manufacturing Technologies: the Future of I...

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IEEE Radar conference

作者： Hester, Jimmy G. D. Kimionis, John Bahr, Ryan Su, Wenjing Tehrani, Bijan Tentzeris, Manos M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781538641675

The authors make a case for a soon-to-be preponderance of radar technologies for communications with devices of the IoT. First, the tag architectures enabling these communications are presented. Novel 24 GHz and mm-wave flexible low-cost chip less RFID and backscatter/reflectarray-based node approaches demonstrating a record measured range of 80m, with a potential maximum range in excess of 1 km, zero to low W power consumption, and Gbps communications rates are presented. In a second part, recent advances in additive-manufacturing technologies enabling large cost reductions for the next generation of integrated radar ICs are reported. Specifically, the combination of 3D and inkjet printing enables the development of fully 3D compact low-cost integrated modules along with high-performance interconnects. These innovations, simultaneously empowering and empowered by radar technologies, present a compelling avenue for the emergence of dense constellations of flexible smart skin nodes for ubiquitous sensing, identification, and localization.

关键词： remote sensing mm-wave Chipless RFID Inkjet printing Flexible electronics Internet of Things Smart Skin

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Using GLIDER for Knowledge Discovery in Climate Science to Visualize, Analyze and Mine Satellite Imagery

Using GLIDER for Knowledge Discovery in Climate Science to V...

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25th ACM/IEEE International conference for high performance computing, Networking, Storage and Analysis (SC)

作者： Graves, Sara Ramachandran, Rahul Berendes, Todd Univ Alabama Informat Technol & Syst Ctr Huntsville AL 35899 USA

ISBN: (纸本)9780769549569;9781467362184

Satellite remote sensing provides an essential source of information for monitoring the earth's atmosphere and climate. Thematic information derived from satellite imagery is applicable to a wide variety of applications at local, regional and global scales. Extracting thematic information using satellite imagery is a non-trivial task that usually requires preprocessing data by applying operations for radiometric and geometric corrections. Analysts need the ability to interactively apply different image enhancement operations to digitally enhance imagery, in order to identify subtle information that might be otherwise missed. Advanced algorithms are needed to allow users to analyze imagery and extract thematic information without having to alter the original data through geographic projections. The Globally Leveraged Integrated Data Explorer for Research (GLIDER) is an easy to use tool for visualization, analysis and mining of satellite imagery. Users can visualize and analyze satellite imagery in its native sensor view, which is an important capability since geographic projections can result in loss of information. A full suite of image processing, pattern recognition and data mining algorithms is provided for image enhancement and analysis. Analysis results and imagery can then be displayed simultaneously and compared using a 3-D globe display that supports image overlays and web map service (WMS) layers. Examples will be shown to illustrate how GLIDER provides views and tools to facilitate knowledge discovery from satellite imagery.

关键词： geoscience and remote sensing Data Mining Supervised Learning

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high performance Automotive Radar Signal Processing on TI's TDA3X Platform

High Performance Automotive Radar Signal Processing on TI's ...

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IEEE Radar conference (RadarConf)

作者： Swami, Pramod Jain, Anshu Goswami, Piyali Chitnis, Kedar Dubey, Aish Chaudhari, Pragat Texas Instruments Inc Automot Processor Business Bangalore Karnataka India Texas Instruments Inc Automot Processor Business Dallas TX USA

ISBN: (纸本)9781467388238

Automotive is an important application of radar. In collision avoidance applications, radar and camera are two main sensors with radar having a healthy share [16]. Usage of radar in the automotive space is expanding beyond range and velocity detection of obstacles to more sophisticated usage of object motion direction estimation [17], precise angular position estimation of obstacles in urban environments [6][7] and ground vehicle localization[15]. As a result, radar processing solutions require complex signal processing with higher programmability. In this paper, we explain the mapping of automotive MIMO radar processing chain on TI's TDA3x platform and highlight the need for a heterogeneous processor architecture with adequate programmability.

关键词： Radar Advanced driver assistance systems autonomous driving System-on-Chip Embedded software high performance computing Multicore processing

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A parallel implementation of nearest neighbor analysis based on GPGPU

A parallel implementation of nearest neighbor analysis based...

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International conference on Geoinformatics

作者： Zhao, Yong Chen, Bin Fang, Yu Huang, Zhou Liu, Yuehu Yu, Hao Institute of Remote Sensing and Geographic Information System Peking University Beijing China

ISBN: (纸本)9781612848488

Nearest neighbor analysis is one of the classic methods to find out the tendency of the observed point dataset. With the explosion of spatial data, conventional implementation of nearest neighbor analysis cannot present high performance towards large amount of dataset. So in this paper, a parallel implementation of nearest neighbor analysis is proposed, with parallelization of computing the nearest neighbor distance of each point. Compared with CPU, now GPU can provide more powerful capacity of processing floating point operations and has more multiprocessors for parallel processing. So we develop the parallel program of nearest neighbor analysis with CUDA (Compute Unified Device Architecture) in terms of GPGPU (General-Purpose computing on Graphics Processing Units). In our experiments, when the number of points is large, the speedup of the parallel implementation can achieve more than 10, compared with the conventional implementation in CPU. © 2011 IEEE.

关键词： Graphics processing unit

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Research on parallel computing of spatial vector data conversion based on common interface

Research on parallel computing of spatial vector data conver...

引用

2012 20th International conference on Geoinformatics, Geoinformatics 2012

作者： Jiang, Xiaohui Li, Manchun Huang, Tao Zhang, Shuai Jin, Zhibing Chen, Zhenjie Department of Geographical Information Science Nanjing University Nanjing Jiangsu 210093 China

ISBN: (纸本)9781467311045

Geospatial data sharing is an increasingly important subject as large amount of spatial data is produced by a variety of sources, stored in incompatible formats, and accessed by different GIS application. Due to the limitations of the techniques, funds, as well as policies, many scholars have been focusing on the design and implementation of the common interface, which is more and more popularly accepted by data normalization communities and GIS software companies. Along with the evolution of computer hardware and software technologies, high performance geographic computing has come into being facing to large-scale geo-spatial data. So the research on parallel strategy for geographic computing becomes a focus. This paper aims to discuss one parallel computing approach of spatial vector data conversion based on common interface. © 2012 IEEE.

关键词： Geographic information systems

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BigGIS: How Big Data Can Shape Next-Generation GIS 3

BigGIS: How Big Data Can Shape Next-Generation GIS

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3rd International conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Yue, Peng Jiang, Liangcun Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China

ISBN: (纸本)9781479941575

The emergence of the "Big Data" concept is changing the way data are managed and analyzed. Traditional GIS software is limited in dealing with big data challenges including versatile data forms, steaming processing, large scale parallel computing, and dynamic mapping and visualization. Significant improvement is needed, which can result in next generation GIS. This paper gives an overview of recent methods in supporting big data management and analysis in geospatial domain. First, it motivates the necessity to develop and use the bigdata-aware GIS software. By reviewing advanced information technologies and approaches, it can assess what operational system framework and approaches are available and applicable in developing bigdata- enabled next-generation GIS - BigGIS. Key considerations for development of BigGIS are highlighted. The results can help identify critical issues and direct future research agenda for next- generation GIS.

关键词： Big Data GIS high performance computing Steam Processing NoSQL

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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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