CAJ | 학술논문

通过分析合成孔径声纳图像中不同目标统计特性参数间的差异，提出了一种利用新特征空间的SAS图像目标分类算法。该算法用马尔可夫随机场分割算法找到感兴趣区域，提取阴影的几何参数和目标的归一化中心矩，并且将目标、阴影、背景之间统计特性的分布参数之差与前两者构成新的特征空间。利用k-均值聚类算法对三类目标进行分类。合成孔径声纳湖试数据验证了算法的有效性。
통과분석합성공경성납도상중불동목표통계특성삼수간적차이，제출료일충이용신특정공간적SAS도상목표분류산법。해산법용마이가부수궤장분할산법조도감흥취구역，제취음영적궤하삼수화목표적귀일화중심구，병차장목표、음영、배경지간통계특성적분포삼수지차여전량자구성신적특정공간。이용k-균치취류산법대삼류목표진행분류。합성공경성납호시수거험증료산법적유효성。
An algorithm of SAS image object classification using a new feature space is proposed on the basis of analyzing dif-ference 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 clus-tered algorithm is applied to classification. The validity of the proposed algorithm is proven by SAS lake-trial.