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.