Research on Method for Massive Pixel-Level Remote Sensing Image Processing Based on Hadoop

Wang, Xiao Yu; Li, Guo Qing; Yu, Wen Yang; Zou, Quan

doi:10.4028/www.scientific.net/amm.333-335.1224

Cited by 3 publications

(1 citation statement)

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“…In Amazon EC2 platform, it implemented a buffer and intersection process using the Amazon Web Services, together with an OGC Web Processing Service. Wang et al transformed the remote sensing image into image information in binary format to support a variety of formats of remote sensing images. Kune et al proposed a two‐phase extension to HDFS and MapReduce programming model, called XHAMI, for the requirements of the overlapped data organization.…”

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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Section: Background and Related Workmentioning

confidence: 99%