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대한원격탐사학회지 = Korean journal of remote sensing v.24 no.1, 2008년, pp.57 - 64   피인용횟수: 2

A Comparative Study of 3D DWT Based Space-borne Image Classification for Differnet Types of Basis Function

Yoo, Hee-Young   (Department of Earth Science Education, Seoul National UniversityUU0000691  ); Lee, Ki-Won   (Department of Information System Engineering, Hansung UniversityUU0001508  ); Kwon, Byung-Doo   (Department of Earth Science Education, Seoul National UniversityUU0000691  );
  • 초록

    In the previous study, the Haar wavelet was used as the sole basis function for the 3D discrete wavelet transform because the number of bands is too small to decompose a remotely sensed image in band direction with other basis functions. However, it is possible to use other basis functions for wavelet decomposition in horizontal and vertical directions because wavelet decomposition is independently performed in each direction. This study aims to classify a high spatial resolution image with the six types of basis function including the Haar function and to compare those results. The other wavelets are more helpful to classify high resolution imagery than the Haar wavelet. In overall accuracy, the Coif4 wavelet has the best result. The improvement of classification accuracy is different depending on the type of class and the type of wavelet. Using the basis functions with long length could be effective for improving accuracy in classification, especially for the classes of small area. This study is expected to be used as fundamental information for selecting optimal basis function according to the data properties in the 3D DWT based image classification.


  • 주제어

    3D DWT .   Basis function .   Classification .   High-resolution imagery.  

  • 참고문헌 (10)

    1. Matlab Wavelet Toolbox User's Guide, http://www. mathworks.com/access/helpdesk/help/pdf_do c/wavelet/wavelet_ug.pdf. 
    2. Yunhao, C., D. Lei, L. Jing, L. Xiaobing, and S. Peijun, 2006. A new wavelet-based image fusion method for remotely sensed data, International Journal of Remote Sensing, 27(7): 1465-1476. 
    3. Boucheron, L. E. and C.D. Creusere, 2005. Lossless wavelet-based compression of digital elevation maps for fast and efficient search and retrieval, Geoscience and Remote Sensing, IEEE Transactions on, 43(5): 1210-1214. 
    4. Tso, B. and R. C. Olsen, 2005. A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process, Remote Sensing of Environment, 97(1): 127-136. 
    5. Koger, C. H., L.M. Bruce, D. R. Shawa, and K.N. Reddyc, 2003. Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max), Remote Sensing of Environment, 86(1): 108-119. 
    6. Yoo, H. Y., K. Lee, and B. D. Kwon, 2007. Application of the 3D Discrete Wavelet Transformation Scheme to Remotely Sensed Image Classification, Korean Journal of Remote Sensing, 23(5): 355-363.     
    7. Daubechies, I., 1992. Ten lectures on wavelets, CBMS, SIAM, 61: 194-202. 
    8. Solbo, S. and T. Eltorft, 2004. Homomorphic Wavelet-Based Statistical Despeckling of SAR Images, Geoscience and Remote Sensing, IEEE Transactions on, 42(4): 711- 720. 
    9. Pajares, G. and J. M. de la Cruz, 2004. A waveletbased image fusion tutorial. Pattern Recognition, 37 (9): 1855-1872. 
    10. Chen, Z. and R. Ning, 2004. Breast volume denoising and noise characterization by 3D wavelet transform, Computerized Medical Imaging and Graphics, 28(5): 235-246. 
  • 이 논문을 인용한 문헌 (2)

    1. 2008. "" 대한원격탐사학회지 = Korean journal of remote sensing, 24(5): 437~444     
    2. 2009. "" 대한원격탐사학회지 = Korean journal of remote sensing, 25(3): 215~223     

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