SAR图像的自动分割方法研究

由于存在相干斑噪声的影响，给SAR图像分割造成很大的困难，该文提出了一种SAR图像的自动分割方法。首先在特征提取阶段，通过计算小波能量提取纹理信息，用邻域统计量提取灰度信息，用保边缘平均灰度提取边缘信息，以确保边缘准确。然后提出一种改进的完全无监督的聚类算法进行图像分割，该算法可以自动确定分割的类型数目。由于该方法充分考虑了SAR图像的纹理、灰度和边缘信息，因而极大地提高了其最终分割性能。实验结果证明了该方法的有效性。

Automatic Segmentation for Synthetic Aperture Radar Images

The multiplicative nature of the speckle noise in SAR images is a big problem in SAR image segmentation. A novel method for automatic segmentation of SAR images is proposed. The wavelet energy is used to extract texture features, the regional statistics is used to extract gray-level features and the edge preserving mean of gray-level features is used to ensure the accuracy of classification of pixels near to the edge. Three representative kinds of features of SAR image are extracted, so the segmentation performance is enhanced. Besides, an improved unsupervised clustering algorithm is proposed for image segmentation, which can determine the number of classes automatically. Segmentation results on real SAR image demonstrate the effectiveness of the proposed method.