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Distributed correlation model mining from remote sensing big data based on gene expression programming

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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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References

  1. Jiang H, Wang K, Wang Y, Gao M, Zhang Y (2016) Energy big data: A survey. IEEE Access 4:3844–3861

    Article  Google Scholar 

  2. Wang K, Mi J, Xu C, Zhu Q, Shu L, Deng D-J (2016) Real-time load reduction in multimedia big data for mobile internet. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 12:76

    Google Scholar 

  3. Wang K, Wang Y, Hu X, Sun Y, Deng D-J, Vinel A, Zhang Y (2017) Wireless big data computing in smart grid. IEEE Wirel Commun 24:58–64

    Article  Google Scholar 

  4. Deren LI, Zhang L, Xia G (2014) Automatic analysis and Mining of Remote Sensing big Data. Acta Geodaetica Et Cartographica Sinica

    Google Scholar 

  5. Traore BB, Kamsu-Foguem B, Tangara F (2016) Data mining techniques on satellite images for discovery of risk areas. Expert Syst Appl

    Google Scholar 

  6. Elkadiri R, Manche C, Sultan M, Al-Dousari A (2016) Development of a coupled spatiotemporal algal bloom model for coastal areas: a remote sensing and data mining-based approach. IEEE J Sel Top Appl Earth Obs Remote Sens

    Google Scholar 

  7. Gonzalez C, Bernabe S, Mozos D, Plaza A (2016) FPGA implementation of an algorithm for automatically detecting targets in remotely sensed hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens:1–10

  8. Zabalza J, Ren J, Zheng J, Han J (2015) Novel two-dimensional singular Spectrum analysis for effective feature extraction and data classification in hyperspectral imaging. IEEE Trans Geosci Remote Sens 53:4418–4433

    Article  Google Scholar 

  9. Kurte KR, Durbha SS, King RL, Younan NH, Vatsavai R (2016) Semantics-enabled framework for spatial image information Mining of Linked Earth Observation Data. IEEE J-STARS 10:1–16

    Article  Google Scholar 

  10. Zhang L, Zhang Q, Du B, Huang X, Tang YY, Tao D (2016) Simultaneous spectral-spatial feature selection and extraction for hyperspectral images. IEEE Trans Cybern:1–13

  11. Sun Z, Fang H, Di L, Yue P (2016) Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques. Comput Geosci 94:56–67

    Article  Google Scholar 

  12. Luo YM, Huang DT, Liu PZ, Feng HM (2016) An novel random forests and its application to the classification of mangroves remote sensing image. Multimedia Tools Appl:1–16

  13. Liu H, He G, Jiao W, Wang G, Peng Y, Cheng B (2016) Sequential pattern mining of land cover dynamics based on time-series remote sensing images. Multimedia Tools Appl:1–24

  14. Wang YP, Shen Y (2015) Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques. Precis Agric 16:99–118

    Article  Google Scholar 

  15. Zhai Y, Cui L, Zhou X, Gao Y, Fei T, Gao W (2013) Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods. Int J Remote Sens 34:2502–2518

    Article  Google Scholar 

  16. Ferreira C (2002) Genetic representation and genetic neutrality in gene expression programming. Advances in Complex Systems 5:389–408

    Article  MATH  Google Scholar 

  17. Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer

  18. Deng S, Aihua Z, Dong Y, Bin H, Lipeng Z (2017) Distributed intrusion detection based on hybrid gene expression programming and cloud computing in cyber physical power system. IET Control Theory Appl

    Google Scholar 

  19. Deng S, Yuan C, Yang J, Zhou A (2017) Distributed Mining for Content Filtering Function Based on simulated annealing and gene expression programming in active distribution network. IEEE Access 5:2319–2328

    Article  Google Scholar 

  20. Yan J, Ma Y, Wang L, Choo KKR, Jie W (2017) A cloud-based remote sensing data production system. Futur Gener Comput Syst

    Google Scholar 

  21. Kurte KR, Bhangale UM, Durbha SS, King RL, Younan NH (2016) Accelerating big data processing chain in image information mining using a hybrid HPC approach. In: IEEE International Geoscience and Remote Sensing Symposium

  22. Cavallaro G, Riedel M, Richerzhagen M, Benediktsson JA (2015) On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE J-STARS 8:1–13

    Google Scholar 

  23. Wang K, Shao Y, Shu L, Han G (2015) LDPA: a local data processing architecture in ambient assisted living communications. IEEE Commun Mag 53:56–63

    Article  Google Scholar 

  24. Wang K, Zhuo L, Shao Y, Yue D, Tsang KF (2016) Toward distributed data processing on intelligent leak-points prediction in petrochemical industries. IEEE Trans Ind Inf 12:2091–2102

    Article  Google Scholar 

  25. Xie X, Yue D, Ma T, Zhu X (2014) Further studies on control synthesis of discrete-time TS fuzzy systems via augmented multi-indexed matrix approach. IEEE transactions on cybernetics 44:2784–2791

    Article  Google Scholar 

Download references

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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Correspondence to Lechan Yang.

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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

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