曲波变换域侧扫声纳图像海底底质分类

Seafloor Sediments Classification of Side-Scan Sonar Imagery in Fast Discrete Curvelet Transform Domain

通过快速曲波变换将海底声纳图像分解为低频子带和各方向子带.用低频子带的标准差描述声图的整体不均匀度,各方向子带的纹理能量测度描述声图纹理的方向性和粗糙性.将构成的特征向量按SVM分类算法用于对侧扫声纳海底图像进行底质分类.对沙、泥、石3种类型海底的侧扫声纳图像进行分类实验,并与空域、小波域的分类方法相比较,表明文中方法能较好地用于海底底质分类.

Fast discrete curvelet transform is performed on the seabed sonar image to obtain low frequency subband coefficients and various directional subband coefficients. Standard deviation is used to describe the image＇s global unevenness in the low frequency subband. The texture energy measure （TEM） is used to process coefficients at each scale in the directional subbands, which describes directivity and roughness of texture. The extracted texture feature vectors are used in the classification of side-scan sonar seafloor images with support vector machines （SVM）. The side-scan sonar images of sand, mud and rock seafloors are classified using the method described in this paper, and other methods. Comparison results show that the presented seafloor classification method has better performance.