基于直方图条件熵的水声数据分类算法

Underwater Acoustic Data Classification Algorithm Based on Conditional Entropy of Histogram

水声数据中目标的不确定性以及各种物质的声纳数据值的杂合程度,使基于直方图最大值和直方图熵的算法都不能很好地解决水声数据分类的问题。为此,提出一种基于直方图条件熵的分类算法。根据水声数据的累积直方图,对水声数据直方图进行分段,使用条件熵判别式分别计算出每一个分段直方图的最佳特征阈值,赋予其相应的不透明度传递函数,以实现对水声数据的分类。实验结果表明,该算法能够较好地实现水声数据的分类,绘制结果中的疑似目标物较清晰,细节信息较丰富。

Because of the target uncertainty in underwater acoustic data and the gray value overlap between various substances,it is hard to solve the classification problem by only using maximum histogram or entropy of histogram.This paper proposes a classification algorithm which is based on the conditional entropy of histogram.Firstly,the underwater acoustic data is segmented according to its cumulative histogram and the largest value of each section histogram is calculated.Secondly,by using the discrimination of conditional entropy of subsection histogram,a classification threshold is calculated for each section.Finally,the classification threshold is assigned an opacity transfer function.The classification of underwater acoustic data is completed.Experimental results show that the algorithm proposed in the paper achieves better effect in classification of underwater acoustic data.The rendering result of suspected target is clearer and the detail information is richer.