水下
人工智能
合成孔径声纳
计算机科学
声纳
计算机视觉
对象(语法)
模式识别(心理学)
影子(心理学)
集合(抽象数据类型)
上下文图像分类
关系(数据库)
遥感
图像(数学)
地质学
数据挖掘
心理学
海洋学
心理治疗师
程序设计语言
标识
DOI:10.1016/j.patcog.2023.109868
摘要
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 object’s 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 reproducability, we share our database.
科研通智能强力驱动
Strongly Powered by AbleSci AI