关键词: 合成孔径声纳, 马尔可夫随机场, 特征提取, k-均值聚类

Abstract: An algorithm of SAS image object classification using a new feature space is proposed on the basis of analyzing difference of statistical features. In the proposed algorithm, Markov random field is used to segment the object and shadow from background, and then the shadow geometrical features and object central moments are computed. Moreover, the differences of statistical parameters of every part are estimated to make up a new feature space with the two mentioned previously. k-mean clustered algorithm is applied to classification. The validity of the proposed algorithm is proven by SAS lake-trial.

Key words: Synthetic Aperture Sonar（SAS）, Markov random field, feature extraction, k-mean cluster