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Hyperspectral image rolling guidance recursive filtering and classification

  • role： First author第一作者
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：cuibinge@qq.com
  • Introduction：崔宾阁，1979年生，男，副教授，研究方向为高光谱遥感的理论和应用。E-mail：cuibinge@qq.com
Binge CUI,  
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Yanan WU,  
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Yong ZHONG,  
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Liwei ZHONG,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：luyan@sdust.edu.cn
  • Introduction：路燕，1975年生，女，副教授，研究方向为智能信息处理。E-mail：luyan@sdust.edu.cn
Yan LU*

résumé

Hyperspectral images have relatively large changes in intra-class spectra, and the “same material, different spectra” phenomena are widespread. Moreover, classification accuracy is low when only the original spectral features are considered, and the “pepper and salt phenomenon” can be observed in the classification result map. In the attempt to improve the classification result, the spectral and spatial information of a hyperspectral image needs to be fully utilized to reduce the spectral changes within the class and expand the spectral difference between classes. Thus, in this study, we propose a spectral–spatial hyperspectral image classification method with rolling guidance recursive filtering. First, principal component analysis (PCA) is used to reduce the dimension of the hyperspectral image, in which the main components with large amounts of information are maintained for the input image of the next step. Second, the Gaussian filter is adopted to blur the input image and eliminate the small-scale structure. Third, recursive filtering is performed on the input image to preserve the edge of the image, in which the guidance image is the blurred image, and then the output result is treated as the new guidance image. The process is repeated iteratively until the large-scale edge is restored. Finally, the extracted feature bands of the hyperspectral image are used for classification by a support vector machine. Indian Pines and University of Pavia datasets were initially used to verify the effectiveness of the proposed rolling guidance recursive filtering (RGRF) method. As shown by the homogeneous regions inFigs. 5 and 6, the noise phenomenon is severe when the extracting feature of PCA is used, whereas the noise is effectively removed when rolling guidance filtering (RGF) and RGRF are utilized. However, at the strong edges, the boundary is blurred and irregular and the spatial smoothness is excessive for RGF. By contrast, the boundary is clear and the strong edges are protected for RGRF. Then, the proposed method is compared with related classification methods by using two real hyperspectral image datasets. The comparative results in Tables 1 and 2 indicate that RGRF can improve classification accuracy more effectively than the other methods. Subsequently, McMemar test was performed to analyze the statistical significance and verify the comparative results. As shown in Tables 3 and 4, the correlations of RGRF with the other methods are statistically significant, and the values exceed p>1.96. The McMemar values inTables 3 and 4 increase with the decrease in number of samples, which indicates that the advantages of using RGRF becomes more obvious in the case of fewer samples. This study presents an improved edge-preserving filtering method named RGRF. Compared with the other filtering methods, RGRF can maintain the edge structures of land covers and eliminate the texture and noise information within these land covers. RGRF effectively uses the recursive filtering method instead of the bilateral filtering method (i.e., of the RGF method) in each iteration. In this manner, the continuous spreading of texture and noise information across the strong edges is avoided. Moreover, considering that image quality is significantly improved after RGRF filtering, the classification accuracy of the hyperspectral image is also greatly enhanced. Classification experiments were performed on two real hyperspectral datasets. The results indicate that the classification accuracy of RGRF is superior to the other hyperspectral image classification methods. Moreover, RGRF can achieve higher classification accuracy when only few training samples are used.

mots-clés

hyperspectral image classification;rolling guidance recursive filtering;feature extraction;principal component analysis

References

  1. 1.
    Altman E I, Marco G and Varetto F. 1994. Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18(3): 505–529
  2. 2.
    Amjady N, Daraeepour A and Keynia F. 2010. Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network. IET Generation, Transmission and Distribution, 4(3): 432–444
  3. 3.
    Balasubramanian M, Schwartz E L, Tenenbaum J B, de Silva V and Langford J C. 2002. The isomap algorithm and topological stability. Science, 295(5552): 7
  4. 4.
    Baldwin J L, Bateman R M and Wheatley C L. 1990. Application of a neural network to the problem of mineral identification from well logs. The Log Analyst, 31(5): 279–293
  5. 5.
    Boggs J L, Tsegaye D T, Coleman T L, Reddy K C and Fahsi A. 2003. Relationship between hyperspectral reflectance, soil nitrate-nitrogen, cotton leaf chlorophyll, and cotton yield: a step toward precision agriculture. Journal of Sustainable Agriculture, 22(3): 5–16
  6. 6.
    Chen C, Zhang L J, Bu J J, Wang C and Chen W. 2010. Constrained Laplacian Eigenmap for dimensionality reduction. Neurocomputing, 73(4/6): 951–958
  7. 7.
    Cui B G, Ma X D, Xie X Y, Ren G B and Ma Y. 2017. Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering. Infrared Physics and Technology, 81: 79–88
  8. 8.
    Cui B G, Ma X D and Xie X Y. 2017. Hyperspectral image de-noising and classification with small training samples. Journal of Remote Sensing, 21(5): 728–738 (
  9. 9.
    Donoho D L and Grimes C. 2003. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences of the United States of America, 100(10): 5591–5596
  10. 10.
    Fauvel M, Chanussot J and Benediktsson J A. 2009. Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas. EURASIP Journal on Advances in Signal Processing, 2009: 783194
  11. 11.
    Foody G M. 2004. Thematic map comparison. Photogrammetric Engineering and Remote Sensing, 70(5): 627–633
  12. 12.
    Gastal E S L and Oliveira M M. 2011. Domain transform for edge-aware image and video processing. ACM Transactions on Graphics, 30(4): 69
  13. 13.
    Gualtieri J A and Cromp R F. 1999. Support vector machines for hyperspectral remote sensing classification//Proceedings of the SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition. Washington, DC, United States: SPIE
  14. 14.
    Huang Q Y, Xiao P F, Feng X Z and Wang Y. 2014. Edge detection from high resolution remote sensing image combining phase congruency with total variation model. Journal of Image and Graphics, 19(3): 439–446 (
  15. 15.
    Hughes G. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1): 55–63
  16. 16.
    Kang X D, Li S T and Benediktsson J A. 2014a. Feature extraction of hyperspectral images with image fusion and recursive filtering. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3742–3752
  17. 17.
    Kang X D, Li S T and Benediktsson J A. 2014b. Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2666–2677
  18. 18.
    Li J, Marpu P R, Plaza A, Bioucas-Dias J M and Benediktsson J A. 2013. Generalized composite kernel framework for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 51(9): 4816–4829
  19. 19.
    Lindeberg T. 1994. Scale-space theory: a basic tool for analyzing structures at different scales. Journal of Applied Statistics, 21(1/2): 225–270
  20. 20.
    Palumbo N F, Harrison G A, Blauwkamp R A and Marquart J K. 2010. Guidance filter fundamentals. Johns Hopkins Apl Technical Digest, 29(1): 60–70
  21. 21.
    Paris S, Kornprobst P and Tumblin J. 2009. Bilateral Filtering. Hanover, MA, USA: Now Publishers Inc.: 1911–1938
  22. 22.
    Peng H Y, Jiang C F, Fang X and Liu J S. 2014. Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data. Applied Mathematics and Computation, 238: 132–140
  23. 23.
    Pontius J, Martin M, Plourde L and Hallett R. 2008. Ash decline assessment in emerald ash borer-infested regions: a test of tree-level, hyperspectral technologies. Remote Sensing of Environment, 112(5): 2665–2676
  24. 24.
    Prasad S and Bruce L M. 2008. Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters, 5(4): 625–629
  25. 25.
    Sakar C O, Kursun O and Gurgen F. 2012. A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method. Expert Systems with Applications, 39(3): 3432–3437
  26. 26.
    Spolaôr N, Cherman E A, Monard M C and Lee H D. 2014. ReliefF for multi-label feature selection//Proceedings of 2013 Brazilian Conference on Intelligent Systems. Fortaleza, Brazil: IEEE
  27. 27.
    van Ruitenbeek F J A, Debba P, van der Meer F D, Cudahy T, van der Meijde M and Hale M. 2006. Mapping white micas and their absorption wavelengths using hyperspectral band ratios. Remote Sensing of Environment, 102(3/4): 211–222
  28. 28.
    Wang L P. 2015. Feature selection algorithm based on conditional dynamic mutual information. International Journal on Smart Sensing and Intelligent Systems, 8(1): 316–337
  29. 29.
    Wang Y, Niu R Q and Yu X. 2010. Anisotropic diffusion for hyperspectral imagery enhancement. IEEE Sensors Journal, 10(3): 469–477
  30. 30.
    Zhang Q, Shen X Y, Xu L and Jia J Y. 2014. Rolling Guidance Filter//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer: 815–830

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