Denoising and SAR Image Classification with K-SVD Algorithm

  • Authors

    • D V.R Mohan
    • I Rambabu
    • B Harish
    https://doi.org/10.14419/ijet.v7i3.3.14481

    Received date: June 21, 2018

    Accepted date: June 21, 2018

    Published date: June 21, 2018

  • Image classification, Multisize Patches, Sparse Representation-based on classification (SRC), K-SVD, Synthetic Aperture Radar Image

  • Abstract

    Synthetic Aperture Radar (SAR) is not only having the characteristic of obtaining images during all-day, all-weather, but also provides object information which is distinctive from visible and infrared sensors. but, SAR images have more speckles noise and fewer bands. This paper propose a method for denoising, feature extraction and classification of SAR images. Initially the image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. Then the Gray Level Histogram (GLH) and Gray Level Co-occurrence Matrix (GLCM) are used for extraction of features. Secondly, the extracted feature vectors from the first step were combined using the correlation analysis to decrease the dimensionality of the feature spaces. Thirdly, Classification of SAR images was done in Sparse Representations Classification (SRC) and Support Vector Machines (SVMs). The results indicate that the performance of the introduce SAR classification method is good. The above mentioned classifications techniques are enhanced and performance parameters are computed using MATLAB 2014a software.

  • References

    1. Liyong Ma, a, Hongbing Ma, b and Ling Liu, C “Speckle Noise Reduction in SAR image based on K-SVD” International Symposi-um on Computers & Informatics (ISCI 2015)
    2. X. Xue, X. Wang, F. Xiang, and H. Wang, “A new method of SAR imagesegmentation based on the gray level co-occurrence matrix and fuzzyneural network,” in Proc. IEEE 6th Int. Conf. Wireless Commun. Netw.Mobile Comput., Sep. 2010, pp. 1–4.
    3. Biao Hou, Member, IEEE, Bo Ren, Guilin Ju, Huiyan Li, Licheng Jiao, Senior Member, IEEE, and Jin Zhao “SAR Image Classifica-tion via Hierarchical Sparse Representation and Multisize Patch Features” IEEE GEOSCIENCE AND REMOTE SENSING LET-TERS,VOL. 13, NO. 1, JANUARY 2016
    4. ] X. Xing, K. Ji, H. Zou, W. Chen, and J. Sun, “Ship classification inTerraSAR-X images with feature space based sparse representa-tion,”IEEEGeosci.RemoteSens.Lett., vol. 10, no. 6, pp. 1562–1566,Nov. 2013.
    5. S.MallatandZ. Zhang, “Matching pursuits with time-frequency dic-tionaries,”IEEETrans.SignalProcess., vol. 41, no. 12, pp. 3397–3415, Dec. 1993.
    6. J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color imagerestoration,” IEEE Trans. Image Process., vol. 17, no. 1, pp. 53–69,Jan. 2008.
    7. J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolutionvia sparse representation,” IEEE Trans. Image Process., vol. 19, no. 11,pp. 2861–2873, Nov. 2010.
    8. X. Zhan, R. Zhang, D. Yin, and C. Huo, “SAR image compression usingmultiscale dictionary learning and sparse representation,” IEEE Geosci.Remote Sens. Lett., vol. 10, no. 5, pp. 1090–1094, Sep. 2013.
    9. Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Hyperspectral image classificationusing dictionary-based sparse representation,” IEEE Trans. Geosci.Remote Sens., vol. 49, no. 10, pp. 3973–3985, Oct. 2011.
    10. L. Zhang, L. Sun, B. Zou, and W. Moon, “Fully polarimetric SAR imageclassification via sparse representation and polarimetric fea-tures,”IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 8,pp. 3923–3932, Aug. 2015.
    11. S. Gou, X. Zhuang, H. Zhu, and T. Yu, “Parallel sparse spectral clusteringfor SAR image segmentation,” IEEE J. Sel. Topics Appl. Earth Observ.,vol. 6, no. 4, pp. 1949–1963, Aug. 2013.
    12. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Ro-bust facerecognition via sparse representation,” IEEE Trans. Pattern Anal. Mach.Intell., vol. 31, no. 2, pp. 210–227, Feb. 2009.
    13. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural fea-tures forimage classification,” IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no. 6,pp. 610–621, Nov. 1973.
    14. L. Fang, S. Li, X. Kang, and J. A. Benediktsson, “Spectral-spatial hyperspectralimage classification via multiscale adaptive sparse rep-resentation,”IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7738–7749,Dec. 2014.
    15. S. C. Zhu, C. E. Guo, Y. Wang, and Z. Xu, “What are textons?” Int J.Comput. Vis., vol. 62, no. 1/2, pp. 121–143, Apr./May 2005.
    16. X. Huang, C. Xie, X. Fang, and L. Zhang, “Combining pixel- and objectbasedmachine learning for identification of water-body types from urbanhigh-resolution remote-sensing imagery,” IEEE J. Sel. Topics Appl. Earth Observ., vol. 8, no. 5, pp. 2097–2110, May 2015
    17. Lee J S. Digital image enhancement and noise filtering by use of local statistics ［ J ］． IEEE Transactions on Pattern Analysis and MachineIntelligence,1980,2( 2) :165-168.
    18. ] Frost S V. A model for radar images and its application to adap-tive digital filtering of multiplicative noise[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, 4(2):157-166
    19. ] Kuan D T, Sawchuk A A, Strand T C, et al. Adaptive noise smoothing filter for images with signal dependent noise[J]. IEEE Tran. On Pattern Analysis Machine Intelligence, 1985, 7(2):165-177.
    20. Lee J. S. Speckle Analysis and Smoothing of Synthetic Aperture Radar Images[J]. Computer Graphics and Image Processing , Vol.17

  • Downloads

  • How to Cite

    V.R Mohan, D., Rambabu, I., & Harish, B. (2018). Denoising and SAR Image Classification with K-SVD Algorithm. International Journal of Engineering and Technology, 7(3.3), 36-40. https://doi.org/10.14419/ijet.v7i3.3.14481