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http://dx.doi.org/10.7780/kjrs.2008.24.5.437

Selecting Optimal Basis Function with Energy Parameter in Image Classification Based on Wavelet Coefficients  

Yoo, Hee-Young (Dept. of Earth Science Education, Seoul National University)
Lee, Ki-Won (Dept. of Information System Engineering, Hansung University)
Jin, Hong-Sung (Dept. of Applied Mathematics, Chonnam National University)
Kwon, Byung-Doo (Dept. of Earth Science Education, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.24, no.5, 2008 , pp. 437-444 More about this Journal
Abstract
Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have tried to enhance classification accuracy. Previous studies have shown that the classification technique based on wavelet transform is more effective than traditional techniques based on original pixel values, especially in complicated imagery. Various basis functions such as Haar, daubechies, coiflets and symlets are mainly used in 20 image processing based on wavelet transform. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we first computed the wavelet coefficients of satellite image using ten different basis functions, and then classified images. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis functions. The energy parameters of wavelet detail bands and overall accuracy are clearly correlated. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.
Keywords
Wavelet transform; Basis function; Energy parameter; Image classification;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Yoo, H. Y., K. Lee and B. D. Kwon, 2009. A Comparative Study of 3D DWT Based Space-borne Image Classification for Different Types of Basis Function, Korean Journal of Remote Sensing, 24(1): 57-64   과학기술학회마을   DOI
2 Pajares, G. and J. M. de la Cruz, 2004. A waveletbased image fusion tutorial, Pattern Recognition, 37(9): 1855-1872   DOI   ScienceOn
3 Myint, S. W., 2003. The Use of Wavelets for Feature Extraction of Cities in Satellite Images, Remotely Sensed Cities (Victor Mesev, editor), Taylors, Frances
4 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   과학기술학회마을   DOI
5 Singh, B. N. and A. K. Tiwari, 2006. Optimal selection of wavelet basis function applied to ECG signal Denoising, Digital Signal Processing, 16: 275-287   DOI   ScienceOn
6 Arivazhagan, S. and L. Ganesan, 2003. Texture segmentation using wavelet transform, Pattern Recognition Letters, 24, 3197-3203   DOI   ScienceOn
7 Daubechies, I., 1992. Ten lectures on wavelets, CBMS, SIAM, 61: 194-202
8 Fukuda, S. and H. Hirosawa, 1999. A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images, IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2282-2286   DOI   ScienceOn