Denoising and SAR Image Classification with K-SVD Algorithm

  • Abstract
  • Keywords
  • References
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  • 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.



  • Keywords

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

  • References

      [1] Liyong Ma, a, Hongbing Ma, b and Ling Liu, C “Speckle Noise Reduction in SAR image based on K-SVD” International Symposium 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 Classification via Hierarchical Sparse Representation and Multisize Patch Features” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,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 representation,”IEEEGeosci.RemoteSens.Lett., vol. 10, no. 6, pp. 1562–1566,Nov. 2013.

      [5] S.MallatandZ. Zhang, “Matching pursuits with time-frequency dictionaries,”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 features,”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, “Robust 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 features 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 representation,”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 adaptive 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




Article ID: 14481
DOI: 10.14419/ijet.v7i3.3.14481

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