Remote sensing image segmentation based on Hadoop cloud platform

Jie Li; Lingling Zhu; Fubin Cao

doi:10.1117/12.2283032

12 January 2018 Remote sensing image segmentation based on Hadoop cloud platform

Jie Li, Lingling Zhu, Fubin Cao

Proceedings Volume 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 106200S (2018) https://doi.org/10.1117/12.2283032
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China

Abstract

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

Citation Download Citation

Jie Li, Lingling Zhu, and Fubin Cao "Remote sensing image segmentation based on Hadoop cloud platform", Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106200S (12 January 2018); https://doi.org/10.1117/12.2283032

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