关键词: 合成孔径雷达（SAR）图像, 小波变换, 广义高斯分布（GGD）, 最大似然估计（MLE）, K-Means

Abstract:

Aiming at addressing the issue that the traditional method which employs gray level co-occurrence matrix as texture feature cannot effectively represent the high frequency information of image edge, this paper proposes a Synthetic Aperture Radar（SAR） image segmentation algorithm based on statistical characteristics of wavelet transform domain combined with multi-resolution analysis of wavelet transform. After wavelet transform, the statistical properties of the image obey the Generalized Gauss Distribution（GGD）. In this paper, two parameters of GGD, [α] and [β], are deduced by Maximum Likelihood（ML） estimation. A fast iterative solution of [β] is proposed by Newton-Raphson method. The texture features of SAR images are taken as [α] and [β], which are segmented by K- Means. The experimental results of typical SAR images show that this algorithm is effective.

Key words: Synthetic Aperture Radar（SAR） image, wavelet transform, Generalized Gauss Distribution（GGD）, Maximum Likelihood Estimation（MLE）, K- Means