规整化SAR图像特征提取

SAR成像算法通常都基于FFT运算,图像分辨率要受到瑞利限的制约。为了提高图像分辨率,目前常用的SAR/ISAR超分辨成像算法大多借助于现代谱估计技术。从解方程的角度考虑,认为有限长数据的高分辨率谱估计是一个欠定方程问题,估计的结果存在"病态"性。在Bayes估计准则下,把信号谱的先验概率密度作为规整项包含进信号频谱的最大后验概率估计中,提高谱估计的分辨率。将这种方法用于SAR图像峰值特征提取,提高了图像分辨率。

Regularized SAR Image Feature Extraction

SAR images generated on the basis of FFT suffer the poor resolution. In super-resolution algorithms of SAR/ISAR image the modern spectral estimation technique is usually used, such as Minimum Variance Method (MVM), AR model, eigen-vector, MUSIC and maximum entropy, to improve the resolution of the image. High-resolution spectral estimation of finite length sample is considered an underdetermined problem. In the framework of Bayesian criteria, a prior probability density function (pdf) of the spectra is included as a regular term in the cost function for MAP estimation. To improve the efficiency of calculation in 2-D case, fast algorithm is derived. The better resolution is achieved by the method while the method is applied to SAR image peak feature extraction.