This paper is organized Imatinib Mesylate buy as follows. In the next section, we will describe quadtree interpretation of SAR imagery and its mixture multiscale autoregressive (MMAR) modeling. In Section 3, we will propose a hybrid method based on the GA algorithm and EM algorithm for the MMAR model. In Section 4, we will present the experimental results. Inhibitors,Modulators,Libraries In Section 5, we will present a short conclusion concerning our algorithm.2.?Wavelet TransformIn signal analysis applications, it is necessary to extract signal features with Fourier transformation, but it is only a time domain transform, which has no time-frequency localization features. Inhibitors,Modulators,Libraries The theory of wavelet transformation was first proposed in the field of multi-resolution analysis; among others, it has been applied to image and signal processing.

A continuous wavelet transform (CWT) can decompose a signal into a set of finite basis functions, which can uncover transient characteristics in the signal. Wavelet analysis is the breaking up of a signal into dilations and translation versions of Inhibitors,Modulators,Libraries the original wavelet, referred to as the mother wavelet. The wavelet must be oscillatory, have amplitudes that quickly decay to zero, and have at least one vanishing moment. Wavelet coefficients Wx(a, ��) are produced through the convolution of a mother wavelet function ��(t) with the analyzed signal f(t), it is:Wx (a,��)=1|a| �� f(t)��(t?��a)dt(1)where a and �� denote Inhibitors,Modulators,Libraries the scale and local center of the analyzing wavelet. By adjusting the scale, a, a series of different frequency components in the signal can be obtained.

Several wavelet mother functions have been proposed in the wavelet theory. Each mother function has its suitable application. In this work, the wavelet employed Batimastat is the Morlet Wavelet, due to its directional selectiveness capability of detecting oriented features, fine tuning to specific frequencies and its good localization in time and frequency [9]. This is a sinusoidal signal modulated by a Gaussian wave. It is characterized for its narrow frequency response, which offers a higher spectral resolution than the Mexican Hat wavelet. This wavelet never is particularly useful for filtering out the background noise of the images. In this paper, the Morlet wavelet is applied as:��a,�� (t)=e?ti2/2 cos(5ti)(2)where ti=t?��a.3.?Wavelet Neural Network3.1. Structure of Wavelet Neural NetworkThe reason for the application of WNN in case of such a problem as classification is that the feature extraction and representation properties of the wavelet transform are merged into the structure of the ANN to further extend the ability to approximate complicated patterns.The WNN can be considered an expanded perceptron in which the neurons of the first layer are replaced by wavelet nodes [10,11].