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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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References

  1. Bachmann CM, Ainsworth TL, Fusina RA (2005) Exploiting manifold geometry in hyperspectral imagery. IEEE Trans Geosci Remote Sens 43(3):441–454

    Article  Google Scholar 

  2. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  3. Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems, pp 585–591

  4. Brand M (2003) Charting a manifold.. In: Advances in neural information processing systems, pp 985–992

  5. Chen SB, Ding CHQ, Luo B (2015) Similarity learning of manifold data. IEEE Trans Cybern 45(9):1744–1756. https://doi.org/10.1109/TCYB.2014.2359984

    Article  Google Scholar 

  6. Chen W, Li X, Wang Y, Liu S (2013) Landslide susceptibility mapping using lidar and dmc data: a case study in the Three Gorges area, China. Environ Earth Sci 70(2):673–685

    Article  Google Scholar 

  7. Chen Y, Wang L, Li F, Du B, Choo KR, Hassan H, Qin W (2017) Air quality data clustering using epls method. Information Fusion 36:225–232

    Article  Google Scholar 

  8. Comon P (1994) Independent component analysis, a new concept? Signal Process 36(3):287–314

    Article  MATH  Google Scholar 

  9. Der Maaten LV, Hinton GE (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  10. Deren L, Liangpei Z, Guisong X (2014) Automatic analysis and data mining of remote sensing data. Acta Geodaetica et Cartographica Sinica 43(12):211–1216

    Google Scholar 

  11. Donoho DL, Grimes C (2003) Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A 100(10):5591–5596

    Article  MathSciNet  MATH  Google Scholar 

  12. Feng R, Zhong Y, Zhang L (2016) Adaptive spatial regularization sparse unmixing strategy based on joint map for hyperspectral remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (12):5791–5805

    Article  Google Scholar 

  13. Gomez D, Montero J (2008) Fuzzy sets in remote sensing classification. Soft Comput 12(3):243–249. https://doi.org/10.1007/s00500-007-0201-z

    Article  Google Scholar 

  14. Han T, Goodenough DG (2005) Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE). In: Proceedings of the 2005 IEEE international geoscience and remote sensing symposium, 2005, IGARSS’05, vol 2, pp 1237–1240

  15. Hassanzadeh A, Kauranne T, Kaarna A. (2016) A multi-manifold clustering algorithm for hyperspectral remote sensing imagery. In: 2016 IEEE International geoscience and remote sensing symposium (IGARSS), pp 3326–3329

  16. He X, Niyogi P (2004) Locality preserving projections, vol 16, pp 153–160

  17. He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: Tenth IEEE international conference on computer vision, 2005. ICCV 2005, vol 2, pp 1208–1213

  18. Hinton GE, Roweis ST (2002) Stochastic neighbor embedding. MIT Press 15:833–840

    Google Scholar 

  19. Huadong G, Lizhe W, Dong L (2014) Scientific big data and digital earth. Chin Sci Bull 59(12):1047–1054

    Article  Google Scholar 

  20. Huang HB, Huo H, Fang T (2014) Hierarchical manifold learning with applications to supervised classification for high-resolution remotely sensed images. IEEE Trans Geosci Remote Sens 52(3):1677–1692. https://doi.org/10.1109/TGRS.2013.2253559

    Article  Google Scholar 

  21. Jiang J, Hu R, Wang Z, Han Z, Ma J (2016) Facial image hallucination through coupled-layer neighbor embedding. IEEE Trans Circuits Syst Video Technol 26(9):1674–1684

    Article  Google Scholar 

  22. Li F, Xu L, Wong A, Clausi DA (2015) Feature extraction for hyperspectral imagery via ensemble localized manifold learning. IEEE Geosci Remote Sens Lett 12(12):2486–2490. https://doi.org/10.1109/LGRS.2015.2487226

    Article  Google Scholar 

  23. Li X, Chen W, Cheng X, Wang L (2016) A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ziyuan-3 stereo satellite imagery. Remote Sens 8(6):514

    Article  Google Scholar 

  24. Ma L, Crawford MM, Tian J (2010) Local manifold learning-based k -nearest-neighbor for hyperspectral image classification. IEEE Trans Geosci Remote Sens 48(11):4099–4109. https://doi.org/10.1109/TGRS.2010.2055876

    Google Scholar 

  25. Palmieri F, Fiore U, Castiglione A (2014) A distributed approach to network anomaly detection based on independent component analysis. Concurrency & Computation Practice & Experience 26(5):1113–1129

    Article  Google Scholar 

  26. Roth V, Steinhage V. (2000) Nonlinear discriminant analysis using kernel functions.. In: Advances in neural information processing systems, pp 568–574

  27. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  28. Samat A, Gamba P, Liu S, Du P, Abuduwaili J (2016) Jointly informative and manifold structure representative sampling based active learning for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(11):6803–6817

    Article  Google Scholar 

  29. Samat A, Gamba P, Liu S, Du P, Abuduwaili J (2016) Jointly informative and manifold structure representative sampling based active learning for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(11):6803–6817. https://doi.org/10.1109/TGRS.2016.2591066

    Article  Google Scholar 

  30. Scholkopf B, Mika S, Burges CJC, Knirsch P, Muller K, Ratsch G, Smola AJ (1999) Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw 10(5):1000–1017

    Article  Google Scholar 

  31. Seung HS, Lee DD (2000) The manifold ways of perception. Science 290 (5500):2268–2269

    Article  Google Scholar 

  32. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE:large-scale information network embedding. In: International conference on world wide web, pp 1067–1077

  33. Tenenbaum JB, De Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  34. Tian T, Zhang Y, Dou H, Tong H (2016) Land-use classification with biologically inspired color descriptor and sparse coding spatial pyramid matching. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4167-7

  35. Tian T, Zhang Y, Choo KKR, Song W (2017) A biologically inspired spatio-chromatic feature for color object recognition. Multimed Tools Appl 76 (18):18,731–18,747. https://doi.org/10.1007/s11042-016-4252-y https://doi.org/10.1007/s11042-016-4252-y

    Article  Google Scholar 

  36. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3 (1):71–86

    Article  Google Scholar 

  37. Vural E, Guillemot C (2016) Out-of-sample generalizations for supervised manifold learning for classification. IEEE Trans Image Process 25(3):1410–1424. https://doi.org/10.1109/TIP.2016.2520368

    Article  MathSciNet  MATH  Google Scholar 

  38. Wang L, Song W, Liu P (2016) Link the remote sensing big data to the image features via wavelet transformation. Clust Comput 19(2):793–810

    Article  Google Scholar 

  39. Wang L, Liu P, Song W, Choo KKR (2017) DUK-SVD: dynamic dictionary updating for sparse representation of a long-time remote sensing image sequence. Soft Computing. https://doi.org/10.1007/s00500-017-2568-9

  40. Weijing S, Peng L, Lizhe W, Ke L (2014) Intelligent processing of remote sensing data: current situation and challenge. J Engl Stud 6(3):259–265

    Google Scholar 

  41. Weinberger KQ, Saul LK (2006) Unsupervised learning of image manifolds by semidefinite programming. Int J Comput Vis 70(1):77–90

    Article  Google Scholar 

  42. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  43. Zhang Y, Zheng X, Liu G, Sun X, Wang H, Fu K (2014) Semi-supervised manifold learning based multigraph fusion for high-resolution remote sensing image classification. IEEE Geosci Remote Sens Lett 11(2):464–468. https://doi.org/10.1109/LGRS.2013.2267091

    Article  Google Scholar 

  44. Zhang Z, Zha H (2005) Principal manifolds and nonlinear dimension reduction via local tangent space alignment. arXiv: Learning 26(1):313–338

    MATH  Google Scholar 

  45. Zhiyong W (2012) Research on dimension reduction method of hyperspectral remote sensing image based on manifold learning. University of Electronic Science and Technology

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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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