Abstract Recognition of seabed sediment is one of the critical foundations of marine exploitation. This paper proposes a probabilistic neural network (PNN) based method to improve the identification accuracy of seabed sediment from side-scan sonar imagery. The feature set of side-scan images consists of two types of features, namely textural features and color features. In this study, partial eigenvalues of the gray co-occurrence matrix are selected as the textural feature, and the color features are represented by color moments. Combining textural features with color features, we get the input matrix, which is then fed into PNN for classification. PNN calculates the distance between the sample eigenvector to be predicted and the training sample eigenvector, then accumulates the probability belonging to a certain category. Finally, PNN outputs the forecasted class of samples eigenvector with the largest posterior probability. It is the first time that PNN has been used in seabed sediment classification from side-scan sonar imagery. Compared with the traditional clustering methods, PNN improved the accuracy of the classification and attained a highest accuracy of 92.2%.

中文翻译：

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基于概率神经网络的侧扫声纳图像海底沉积物识别方法

摘要 海底沉积物的识别是海洋开发的重要基础之一。本文提出了一种基于概率神经网络（PNN）的方法，以提高侧扫声纳图像对海底沉积物的识别精度。侧扫图像的特征集由两类特征组成，即纹理特征和颜色特征。本研究选取灰色共生矩阵的部分特征值作为纹理特征，颜色特征用颜色矩表示。结合纹理特征和颜色特征，我们得到输入矩阵，然后将其输入 PNN 进行分类。PNN计算待预测样本特征向量与训练样本特征向量之间的距离，然后累加属于某个类别的概率。最后，PNN 输出具有最大后验概率的样本特征向量的预测类别。这是首次将 PNN 用于侧扫声纳图像的海底沉积物分类。与传统的聚类方法相比，PNN 提高了分类的准确率，最高达到了 92.2% 的准确率。