In order to improve the recognition effect and on the basis of analyzing the characteristic of MSTAR SAR image, a method of discrete wavelet analysis is proposed to extract features.
为了进一步提高MSTARSAR目标的识别效果，在分析了MSTAR SAR图像特点的基础上，提出了一种利用离散小波分解提取目标特征的方法。
It is then subject to such mathematical indignities as wavelet decomposition, multi-resolution Fourier analysis, polyphase filtering and discrete cosine transformation.
接下来它还要经过一些数学处理程序，比如小波分解，多重分辨率傅立叶分析，多相过滤，离散余弦变换等。
Based on mentioned-above wavelet analysis theories, two-dimension biorthogonal wavelet and the implementation of wavelet transform on discrete images have been discussed.
在以上小波分析理论的基础上进一步讨论了二维正交小波变换和离散图像的小波变换实现。
An efficient implementation of the discrete form of the wavelet transform on digital signal processors (DSPs) is especially interesting for the analysis of real time signals.
在实时信号分析中，离散小波变换在DSP上的有效应用受到了特别的关注。
Describes the wavelet function, continuous wavelet transform, discrete wavelet transform, dyadic wavelet transform, wavelet transform, multiresolution analysis, wavelet transform theory.
叙述了小波基函数、连续小波变换、离散小波变换、二进小波变换、二维小波变换、多分辨分析小波变换理论。
Describes the wavelet function, continuous wavelet transform, discrete wavelet transform, dyadic wavelet transform, wavelet transform, multiresolution analysis, wavelet transform theory.
叙述了小波基函数、连续小波变换、离散小波变换、二进小波变换、二维小波变换、多分辨分析小波变换理论。
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