Parallel Processing with MPI for Inter-band Registration in Remote Sensing

Kim, Tae‐Young; Choi, Myung-Jin; Chae, Tae-Byeong

doi:10.1109/icpads.2011.95

Cited by 4 publications

(3 citation statements)

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“…Supercomputers have been widely used in RS applications to accelerate and scale the process of image mosaicking [30], [31], classification [32]- [37], object detection [38], [39], clustering [40]- [42], interband registration [43], superresolution [44], data fusion [16], compression [45], feature selection/extraction [46]- [48], spectral unmixing [49], data assimilation [50], and scalable-processing workflows [51]- [56]. In the context of HPC, there were also important efforts in academic journals and conferences, launching multiple special issues devoted to the processing and analysis of RS data [57]- [60].…”

Section: Supercomputingmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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T he High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

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Section: Supercomputingmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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“…In global change research, the high performance and high throughput computing is critical . Traditionally, most parallel applications achieve fine‐grained parallelism using MPI implemented on computer clusters or a grid infrastructure. Generally, writing programs in MPI requires sophisticated skills for the users.…”

Section: Background and Related Workmentioning

confidence: 99%

MapReduce functions to remote sensing distributed data processing—Global vegetation drought monitoring as example

Zou

2018

Softw Pract Exp

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Global change models for different applications are developed, according to the principle of remote sensing technology. Data for these models are generally remote sensing image, which is multiplatform, multidimentional, multiband, and multisource. Moreover, such data may be in different parts of the world and perhaps up to terabyte or petabyte level. Therefore, a data-intensive computing problem in the global change has emerged. Distributed computing infrastructures are suitable to store large-scale datalike satellite images that have to be written only once and read frequently. The emergence of the cloud computing technology brings new information architecture, and global change models implemented in the cloud platform provide users with stable, effective, on-demand cloud computing services. In this paper, the experiment is carried out on the cloud framework based on open cloud computing platform-Hadoop.In addition, on this framework, it achieves a prototype example for monitoring global vegetation drought conditions. Oriented to a variety of remote sensing data, we propose an abstract data format to achieve the unified description of remote sensing data. The data abstraction is to discretize the multidimensional remote sensing data for easy-distributed storage and computation. Using MapReduce paradigm, the complexity of remote sensing algorithms is resolved.Experimental results show that based on the parallel programming method, good scalability changing with the amount of processed data in the Hadoop distributed environment.In the era of big data, there are plenty of remote sensing information to choose. Reasonable choice and extraction of the most useful value are necessary requirements for the development of computer technology and network technology. It captures data from a huge remote sensing database then processes, transmits, analyzes these data, and extracts useful information to help users make a decision within a reasonable time, and if necessary, provides and shares the visualizations. The purpose of big data 1 technology is to extract intrinsic information from a huge data collection. The global change model is put forward for the global change issues, such as the vegetation drought-monitoring model, the dust storm monitoring model, the aerosol optical depth inversion model, and so on. From the data perspective, the global change model usually requires multisource, 2-4 multiplatform, 5,6 multisensor, 7 multiresolution, 8 and multispectral 9 remote sensing image data located across the globe, and a large number of gigabytes even terabytes of remote sensing image data are being generated every day over the world. From the computing point of view, global change models are usually composed of several remote sensing image processing algorithms. Thus, the simulation of global change models are typical data-intensive computing, which have frequent data input/output (I/O) but a relatively simple computing process.Today, data-intensive computing researches are often about distributed storage and computing sy...

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“…Recently, great efforts have been laid on the incorporation of MPI-enabled paradigm with remote sensing data processing in the large scale scenarios. Some related works go with Plaza et al presented parallel processing algorithms for hyperspectral imageries ( [27]), Zhao et al ([28]) implemented soil moisture estimation in parallel on PC cluster, as well as MPIenabled implementing of image mosaicking ( [29], fusion ( [30]) and band registration ( [31]). Obviously, benefiting from the efforts and developments conducted in HPC platforms, plenty of RS applications have enhanced their computational performance in a significant way ( [5]).…”

Section: Cluster Computing For Rs Data Processingmentioning

confidence: 99%

pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang

Yan

et al. 2018

Future Generation Computer Systems

169

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With the increasing requirement of accurate and up-to-date resource & environmental information for regional and global monitoring, large-region covered multi-temporal, multi-spectral massive remote sensing (RS) datasets are exploited for processing. The remote sensing data processing generally follows a complex multi-stage processing chain, which consists of several independent processing steps subject to types of RS applications. In general the RS data processing for regional environmental and disaster monitoring are recognized as typical both compute-intensive and data-intensive applications.To solve the aforementioned issues efficiently, we propose pipsCloud which combine recent Cloud computing and HPC techniques to enable large-scale RS data processing system as on-demand real-time services. Benefiting from the ubiquity, elasticity and high-level of transparency of Cloud computing model, the massive RS data managing and data processing for dynamic environmental monitoring are all encapsulate as Cloud with Web interfaces. Where, a Hilbert-R + based data indexing mechanism is employed for optimal query and access of RS imageries, RS data products as well as interim data. In the core platform beneath the Cloud services, we provide a parallel file system for massive high-dimensional RS data and offers interfaces for intensive irregular RS data accessing so as to provide improved data locality and optimized I/O performance. Moreover, we adopt an adaptive RS data analysis workflow manage system for on-demand workflow construction and collaborative execution of distributed complex chain of RS data processing, such as forest fire detection, mineral resources and coastline monitoring. Through the experimental analysis we have show the efficiency of the pipsCloud platform.

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Parallel Processing with MPI for Inter-band Registration in Remote Sensing

Cited by 4 publications

References 4 publications

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

MapReduce functions to remote sensing distributed data processing—Global vegetation drought monitoring as example

pipsCloud: High performance cloud computing for remote sensing big data management and processing

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