基于多波束背向散射数据的海底底质分类

SEABED SEDIMENT CLASSIFICATION BASED ON MULTIBEAM BACKSCATTER DATA

基于精细处理后的多波束数据生成背向散射影像图,利用灰度纹理共生阵提取影像纹理特征参数,采用支持向量机的神经网络(SVM)对背向散射影像进行底质分类研究。通过实测大面积、海量数据对该方法进行评价和验证,结果表明,该方法可获得比传统分类方法更高的分类精度,这种面状的分类弥补了传统点状分类的缺陷,使得大规模、大范围、高效快速的海底底质分类成为可能,为海洋地质调查、海洋工程建设、海底矿产资源开发等提供一种新型的科学的技术方法和可靠的地质基础资料。

This paper introduces a method for seabed sediment classification. At first, texture features are extracted by gray level co-occurrence matrix based on backscatter image of multibeam data, then seabed sediment classification is carried out with supporting vector machine（SVM） using the backscat- ter combined with backscatter image. The result of classification by this method is better than the traditional method based on point data according to our practice. It will certainly make the regional marine geological survey more efficient and reliable.