Author(s)

Yang Cheng

Corresponding Author

Yang Cheng

Abstract

As one of the important information sources reflecting the topography and landform of the seabed, the sonar image of the seabed bottom quality has great guiding significance and research value in the field of deep sea exploration. Nowadays, image recognition technology is paid more and more attention. Technical methods such as extracting the features of the target area from the image for target segmentation and feature enhancement, or combining with effective classifiers for feature classification and target recognition are applied to all aspects of real life and production. Obtaining image information and establishing a multi-dimensional objective description of the image has far-reaching significance and value. Based on a comprehensive understanding of the imaging principles and image characteristics of the side-scan sonar bottom quality image, this paper takes into account factors such as the seabed environment, noise characteristics and feature types, and forms a process in accordance with the procedures of denoising enhancement, feature extraction, and recognition and classification. The complete system of sea bottom quality data processing and analysis based on side scan sonar images reveals the characteristic differences and classification criteria of multiple types of sea bottom quality data, and provides theoretical methods and technical support for the accurate identification and correct classification of sea bottom quality information.

Keywords

Side scan sonar, Target recognition, Fine processing