The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated feature selection approach for SAS image segmentation. The chosen features and resulting segmentation from the image will be assessed based on a select quantitative clustering validity criterion and the subset of the features that reach a desired threshold will be used for the segmentation process.

翻译：在这项工作中,我们调查并介绍了SAS图像分割自动特征选择方法的结果,将根据选定的定量组合有效性标准对图像的选定特征和由此产生的分化进行评估,在分层过程中将使用达到理想阈值的子集特征。