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Improved t-SNE based manifold dimensional reduction for remote sensing data processing

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Abstract

In our increasingly “data-abundant” society, remote sensing big data perform massive, high dimension and heterogeneity features, which could result in “dimension disaster” to various extent. It is worth mentioning that the past two decades have witnessed a number of dimensional reductions to weak the spatiotemporal redundancy and simplify the calculation in remote sensing information extraction, such as the linear learning methods or the manifold learning methods. However, the “crowding” and mixing when reducing dimensions of remote sensing categories could degrade the performance of existing techniques. Then in this paper, by analyzing probability distribution of pairwise distances among remote sensing datapoints, we use the 2-mixed Gaussian model(GMM) to improve the effectiveness of the theory of t-Distributed Stochastic Neighbor Embedding (t-SNE). A basic reducing dimensional model is given to test our proposed methods. The experiments show that the new probability distribution capable retains the local structure and significantly reveals differences between categories in a global structure.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 41471368, No. 41571413) and the Hubei Provincial Natural Science Foundation of China (No.2017CFB279).

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Authors and Affiliations

  1. College of Computer Science, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Weijing Song & Lizhe Wang

  2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, People’s Republic of China

    Peng Liu

  3. Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249-0631, USA

    Kim-Kwang Raymond Choo

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  1. Weijing Song
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  2. Lizhe Wang
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  3. Peng Liu
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  4. Kim-Kwang Raymond Choo
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Correspondence to Lizhe Wang.

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Song, W., Wang, L., Liu, P. et al. Improved t-SNE based manifold dimensional reduction for remote sensing data processing. Multimed Tools Appl 78, 4311–4326 (2019). https://doi.org/10.1007/s11042-018-5715-0

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