A New Approach to Improve the Cluster-based Parallel Processing Efficiency of High-Resolution Remotely Sensed Image

Shen, Zhanfeng; Luo, Jiancheng; Wu, Wei; Hu, Xiaodong

doi:10.1007/s12524-011-0166-3

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Because the LIC-seeking computing procedures for lake polygons are independent of each other, we may divide the features of vector data into several parts, and each part is calculated and completed in a computing core. Different data partition methods may lead to different acceleration ratios for the algorithm [ 33 , 34 ]; thus, in this study we placed particular emphasis on the data partition policy for vector data.…”

Section: Methodsmentioning

confidence: 99%

A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration

Shen

Sheng

et al. 2015

PLoS ONE

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When conducting image registration in the U.S. state of Alaska, it is very difficult to locate satisfactory ground control points because ice, snow, and lakes cover much of the ground. However, GCPs can be located by seeking stable points from the extracted lake data. This paper defines a process to estimate the deepest points of lakes as the most stable ground control points for registration. We estimate the deepest point of a lake by computing the center point of the largest inner circle (LIC) of the polygon representing the lake. An LIC-seeking method based on Voronoi diagrams is proposed, and an algorithm based on medial axis simplification (MAS) is introduced. The proposed design also incorporates parallel data computing. A key issue of selecting a policy for partitioning vector data is carefully studied, the selected policy that equalize the algorithm complexity is proved the most optimized policy for vector parallel processing. Using several experimental applications, we conclude that the presented approach accurately estimates the deepest points in Alaskan lakes; furthermore, we gain perfect efficiency using MAS and a policy of algorithm complexity equalization.

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Section: Methodsmentioning

confidence: 99%

A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration

Shen

Sheng

et al. 2015

PLoS ONE

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Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

Xia

Luo

et al. 2018

J Indian Soc Remote Sens

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A High-Efficiency Fusion Method of Multi-Spectral Image and Panchromatic Image

Xia

Wang

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the development of modern remote sensing technology, a variety of earth observation satellites could continue to tremendously provide image data of different spatial resolution, time resolution, spectral resolution remote sensing, and the remote sensing data obtained is increasing with great capacity, which forms multi-source image pyramid in the same area. To play the advantages of a variety of remote sensing data, the application of remote sensing image fusion is a very important choice. When remote sensing data is large, fusion is large in computing capacity and time-consuming, so it is difficult to carry out rapid, real-time fusion. However, in some remote sensing applications, such as disaster prevention and relief quick, etc., timely fusion is required. Based on image fusion method of principal component analysis (PCA) and the advantage of parallel computing, a high-efficiency fusion method of multi-spectral image and panchromatic image is proposed. Beijing-1 Micro-satellite is a high-performance small satellite for earth observation，With Beijing-1 Micro-satellite remote sensing images as the experimental data, it is proved that good fusion results of multi-spectral image and panchromatic image can be obtained with the proposed method, and the fusion speed is also fast. At the same time, some measures of improving the efficiency of parallel image fusion are also discussed. 1．INTRODUCTIONWith the development of modern remote sensing technology, a variety of earth observation satellites could continue to tremendously provide image data of different spatial resolution, time resolution, spectral resolution remote sensing, and the remote sensing data obtained is increasing with great capacity, which forms multi-source image pyramid in the same area. In such cases, in the description of the same object with multiple-source remote sensing information, how to conduct effective data processing, and play the advantages of a variety of remote sensing data, the application of remote sensing image fusion is a very important choice. When remote sensing data is large, fusion is large in computing capacity and time-consuming, so it is difficult to carry out rapid, real-time fusion. However, in some remote sensing applications, such as disaster monitoring, prevention and relief, dynamic monitoring on plant diseases and insect pests of agriculture, etc., rapid even real-time fusion is required [1][2][3][4][5][6][7].In high-performance computing, parallel cluster computing system has a higher cost-effective ratio and good expansibility, which can meet different large-scale computing problems, and increasing importance has been attached to it. The PCA image fusion method is an important one in image fusion. In the paper, based on the analysis of the process of the PCA image fusion method, and with the advantages of parallel computing, a high-efficiency fusion method of multi-spectral image and panchromatic image is studied and proposed. Beijing-1 Micro-satellite is a high-performance small satellite for earth ob...

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A New Approach to Improve the Cluster-based Parallel Processing Efficiency of High-Resolution Remotely Sensed Image

Cited by 3 publications

References 24 publications

A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration

A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

A High-Efficiency Fusion Method of Multi-Spectral Image and Panchromatic Image

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