合成孔径雷达(synthetic aperture radar, SAR)图像分割是河流检测与识别的关键步骤，为了进一步提高河流SAR图像分割的准确性，提出一种基于Shearlet变换、Krawtchouk矩不变量及模糊局部信息C 均值聚类的河流SAR 图像分割方法. 首先，对河流SAR图像进行Shearlet 分解，提取其纹理特征，构成特征向量的前半部分；然后，计算河流SAR 图像的Krawtchouk 矩不变量，作为其形状特征，构成特征向量的后半部分；最后，利用模糊局部信息C 均值算法依照上述特征向量进行聚类，由此得到河流SAR 图像分割结果. 大量实验结果表明，与近年来提出的脉冲耦合神经网络结合最大方差比准则分割法、Gabor 小波变换结合模糊C 均值聚类分割法、FLICM 聚类分割法相比，所提出的方法在主观视觉效果以及客观定量评价指标误分割率上均有明显优势，且分割河流SAR 图像更加准确.
Segmentation of synthetic aperture radar (SAR) images is a key procedure in river detection and recognition. To further improve accuracy of SAR image segmentation for rivers, a segmentation method is proposed based on Shearlet transform, Krawtchouk moment invariants, and fuzzy local information C-means (FLICM). The SAR image is first decomposed with Shearlet transform, and its texture features are extracted as the first part of the feature vector. Krawtchouk moment invariants of the image are then calculated to obtain corresponding shape features used as the second part of the feature vector. Finally,
the image is clustered based on FLICM algorithm using the extracted feature vector. Thus segmentation of the river SAR image is obtained. A large number of experiments are performed. The results are compared with several recently proposed methods based on pulse coupled neural network (PCNN) combined with maximum variance ratio, and Gabor wavelet transform combined with fuzzy C-means (FCM) and FLICM clustering. It has been shown that the proposed method has clear advantages both in subjective visual effects and in terms of objective evaluation indicator such as segmentation error rate. The method can
provide river segmentation with better accuracy.
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