This paper presents a method for feature extraction of high resolution remote sensing image which is based on the
statistical model of the marginal distribution of wavelet coefficients. First, the wavelet is used to transform high
resolution remote sensing images into frequent domain. Then, Generalized Gaussian density(GGD) is used to accurately
model the marginal distribution of wavelet subband coefficients (wavelet coefficients histogram) followed by the
establishment of the remote sensing image texture feature vector. Finally, the Kullback Leibler distance (KLD) is
computed between the texture feature vectors as similarity measurement(SM), and the output is ordered by the result of
the SM. Experimental results show that this method is effective and efficient, and the image feature can be well
represented by this texture feature vector. The advantage of this method is that the SM step can be computed entirely on
the estimated model parameters, which has solid theoretic background, so that it can meet the requirements of the CBIR
application.
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