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Subspace-based multitask learning framework for hyperspectral imagery classification

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Abstract

Subspace-based models have been widely applied for hyperspectral imagery applications, especially for classification. The main principle of these methods is based on the fact that the original image can approximately lie on a lower-dimensional subspace. However, due to the existence of mixed samples, the subspace projection is unstable and affected by the selection of training samples, such that may lead to poor characterization and classification performances. In order to improve the robustness and characterization ability of the subspace-based classification models, this paper proposes a novel subspace-based multitask learning framework. In particular, the original image is first projected to the multiple subspaces in different branches. Then, the support vector machine (SVM) classifier is applied in each branch to deal with the projected data sets. With a consideration of integrating the spatial information, an extended step is provided including the process of a Markov Random Field (MRF) based on the result of SVM. Finally, the classification result is obtained by a decision fusion process. Experimental results on three real hyperspectral data sets demonstrate the improvements on classification performance of the proposed methods over other related methods.

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References

  1. Borel C, Gerslt S (1994) Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sens Environ 47(3):403–416

    Article  Google Scholar 

  2. Camps-Valls G, Bruzzone L (2005) Kernel-based methods for hyperspectral image classification. IEEE Trans Geosci Remote Sens 43(6):1351–1362

    Article  Google Scholar 

  3. Chang CI (2003) Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer Science and Business Media, New York

    Book  Google Scholar 

  4. Chang C, Zhao X, Althouse MLG, Pan JJ (1998) Least squares subspace projection approach to mixed pixel classification for hyperspectral images. IEEE Trans Geosci Remote Sens 36(3):898–912

    Article  Google Scholar 

  5. Chen C, Li W, Su H, Liu K (2014) Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens 6(6):5795–5814

    Article  Google Scholar 

  6. Chen C, Li W, Tramel EW, Cui M, Prasad S, Fowler JE (2014) Spectral–spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 7(4):1047–1059

    Article  Google Scholar 

  7. Du B, Wang S, Xu C, Wang N, Zhang L, Tao D (2018) Multi-Task learning for blind source separation. IEEE Trans Image Process 27(9):4219–4231

    Article  MathSciNet  MATH  Google Scholar 

  8. Du B, Wang Z, Zhang L, Zhang L, Liu W, Shen J, Tao D (2017) Exploring representativeness and informativeness for active learning. IEEE Transactions on Cybernetics 47(1):14–26

    Article  Google Scholar 

  9. Du B, Zhang M, Zhang L, Hu R, Tao D (2017) PLTD: Patch-based low-rank tensor decomposition for hyperspectral images. IEEE Transactions on Multimedia 19(1):67–79

    Article  Google Scholar 

  10. Farag AA, Mohamed RM, El-Baz A (2005) A unified framework for map estimation in remote sensing image segmentation. IEEE Trans Geosci Remote Sens 43(7):1617–1634

    Article  Google Scholar 

  11. Fauvel M, Tarabalka Y, Benediktsson JA, Chanussot J, Tilton JC (2013) Advances in spectral-spatial classification of hyperspectral images. Proc IEEE 101(3):652–675

    Article  Google Scholar 

  12. Fjortoft R, Delignon Y, Pieczynski W, Sigelle M, Tupin F (2003) Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields. IEEE Trans Geosci Remote Sens 41(3):675–686

    Article  Google Scholar 

  13. Gao L, Li J, Khodadadzadeh M, Plaza A, Zhang B, He Z, Yan H (2015) Subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(2):349–353

    Article  Google Scholar 

  14. Gao L, Yu H, Zhang B, Li Q (2016) Locality-preserving sparse representation-based classification in hyperspectral imagery. J Appl Remote Sens 10(4):042004

    Article  Google Scholar 

  15. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741

    Article  MATH  Google Scholar 

  16. Jia X, Richards JA (2008) Managing the spectral-spatial mix in context classification using Markov random fields. IEEE Geosci Remote Sens Lett 5(2):311–314

    Article  Google Scholar 

  17. Jiang J, Chen C, Song X, Cai Z (2016) Hyperspectral image classification using set-to-set distance. In: Proceedings of the ICASSP: 3346–3350

  18. Jiang J, Chen C, Yu Y, Jiang X, Ma J (2017) Spatial-aware collaborative representation for hyperspectral remote sensing image classification. IEEE Geosci Remote Sens Lett 14(3):404–408

    Article  Google Scholar 

  19. Jiang J, Ma J, Chen C, Wang Z, Cai Z, Wang L (2018) SuperPCA: A superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery. IEEE Trans Geosci Remote Sens 56(8):4581–4593

    Article  Google Scholar 

  20. Keshava N, Mustard J (2002) Spectral unmixing. IEEE Signal Process Mag 19(1):44–57

    Article  Google Scholar 

  21. Kirkpatrick S (1984) Optimization by simulated annealing: Quantitative studies. J Stat Phys 34(5–6):975–986

    Article  MathSciNet  Google Scholar 

  22. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  23. Li J, Bioucas-Dias JM, Plaza A (2010) Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans Geosci Remote Sens 48(11):4085–4098

    Google Scholar 

  24. Li J, Bioucas-Dias JM, Plaza A (2012) Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans Geosci Remote Sens 50(3):809–823

    Article  Google Scholar 

  25. Li W, Chen C, Su H, Du Q (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 53(7):3681–3693

    Article  Google Scholar 

  26. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790

    Article  Google Scholar 

  27. Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10(3):61–74

    Google Scholar 

  28. Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P, Gualtieri A (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122

    Article  Google Scholar 

  29. Plaza A, Martinez P, Perez R, Plaza J (2004) A quantitative and comparative analysis of endmember extraction algorithms. IEEE Trans Geosci Remote Sens 42(3):650–663

    Article  Google Scholar 

  30. Richards JA, Jia X (2006) Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag, Berlin

    Google Scholar 

  31. Serpico S, Moser G (2007) Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Trans Geosci Remote Sens 45(2):484–495

    Article  Google Scholar 

  32. Vapnik V (1998) Statistical Learning Theory. Wiley, New York

    MATH  Google Scholar 

  33. Watanabe S, Lambert PF, Kulikowski CA, Buxton JL, Walker R (1967) Evaluation and selection of variables in pattern recognition. In: Computer and Information Sciences II. Academic Press: New York, NY, USA: 91–122

  34. Xie J, Hone K, Xie W, Gao X, Shi Y, Liu X (2013) Extending twin support vector machine classifier for multi-category classification problems. Intell Data Anal 17(4):649–664

    Article  Google Scholar 

  35. Yu H, Gao L, Li J, Li S, Zhang B, Benediktsson JA (2016) Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields. Remote Sens 8(4):355

    Article  Google Scholar 

  36. Yu H, Gao L, Liao W, Zhang B (2018) Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification. Sensors 18(6):1695

    Article  Google Scholar 

  37. Yu H, Gao L, Liao W, Zhang B, Pižurica A, Philips W (2017) Multiscale superpixel-level subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 14(11):2142–2146

    Article  Google Scholar 

  38. Yu H, Gao L, Zhang B (2018) Union of random subspace-based group sparse representation for hyperspectral imagery classification. Remote Sens Lett 9(6):534–540

    Article  Google Scholar 

  39. Zhang B, Li S, Jia X, Gao L, Peng M (2011) Adaptive Markov random field approach for classification of hyperspectral imagery. IEEE Geosci Remote Sens Lett 8(5):973–977

    Article  Google Scholar 

  40. Zhong Y, Lin X, Zhang L (2014) A support vector conditional random fields classifier with a Mahalanobis distance boundary constraint for high spatial resolution remote sensing imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 7(4):1314–1330

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 41722108, No. 91638201 and No. 61501017.

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Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Haoyang Yu, Lianru Gao & Bing Zhang

  2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China

    Haoyang Yu & Bing Zhang

  3. Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China

    Jun Li

Authors
  1. Haoyang Yu
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  2. Lianru Gao
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  3. Jun Li
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  4. Bing Zhang
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Correspondence to Lianru Gao.

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Yu, H., Gao, L., Li, J. et al. Subspace-based multitask learning framework for hyperspectral imagery classification. Multimed Tools Appl 79, 8887–8909 (2020). https://doi.org/10.1007/s11042-018-7010-5

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  • DOI: https://doi.org/10.1007/s11042-018-7010-5

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