Abstract
With the continuous development of remote sensing techniques, an enormous amount of remote sensing data is collected by observing the earth. The method by which researchers mine the relevant knowledge accurately and efficiently from remote sensing big data remains a topic of interest. The existing model mining algorithms usually rely on a priori knowledge, and cannot meet the actual needs of remote sensing data mining. This paper proposes distributed correlation model mining from remote sensing big data based on gene expression programming (DCMM-GEP) combined with cloud computing to find a better model for remote sensing big data using an abnormal value recognition algorithm based on residual sum of residual (AVR-RSR) and a global model generation algorithm for remote sensing big data based on linear least squares (GMGRS-LLS). Comparative experiments show that DCMM-GEP outperforms both distributed correlation model mining based on genetic programming and genetic algorithms, showing better average time-consumption, R-square values and prediction accuracy. The comparative results also show that with an increasing number of computing nodes, DCMM-GEP has a good speed-up ratio and scale-up ratio.
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Acknowledgments
This research was supported by the Key Project of the National Social Science Foundation of China (Grant No.: 11&zd167) and the General Project of the National Natural Science Found of China (Grant No.: 41471300).
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This article is part of the Topical Collection: Special Issue on Big Data Networking
Guest Editors: Xiaofei Liao, Song Guo, Deze Zeng, and Kun Wang
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Yang, L., Qin, Z. Distributed correlation model mining from remote sensing big data based on gene expression programming. Peer-to-Peer Netw. Appl. 11, 1000–1011 (2018). https://doi.org/10.1007/s12083-017-0589-x
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DOI: https://doi.org/10.1007/s12083-017-0589-x