Combining synthetic aperture sonar (SAS) imagery with optical images for underwater object classification has the potential to overcome challenges such as water clarity, the stability of the optical image analysis platform, and strong reflections from the seabed for sonar-based classification. In this work, we propose this type of multi-modal combination to discriminate between man-made targets and objects such as rocks or litter. We offer a novel classification algorithm that overcomes the problem of intensity and object formation differences between the two modalities. To this end, we develop a novel set of geometrical shape descriptors that takes into account the geometrical relation between the objects shadow and highlight. Results from 7,052 pairs of SAS and optical images collected during several sea experiments show improved classification performance compared to the state-of-the-art for better discrimination between different types of underwater objects. For reproducibility, we share our database.

使用合成孔径声纳（SAS）图像和光学图像相结合的多模态组合来区分人工目标和岩石等对象，提出了一种创新的分类算法，可以解决两种模态之间强度和对象形成差异的问题，并使用一组新的几何形状描述符来获取影子和高光之间的几何关系，通过对7,052对SAS和光学图像的实验结果表明其在不同类型的水下目标区分方面相对于现有技术有更好的分类性能，并分享我们的数据库以实现可重复性。

结合SAS和光学转SAS图像的水下物体分类