Underwater multi-source acoustic image substrate classification method and system based on decision-making level fusion

Abstract

The invention discloses an underwater multi-source acoustic image substrate classification method and system based on decision-making level fusion. The method comprises the following steps: acquiringa geomorphic image, a topographic image and a stratum profile image under a unified space coordinate system; determining a landform segmentation image, a terrain segmentation image and a stratum profile segmentation image; performing feature extraction on each segmented image to obtain landform features, topographic features and stratum features; respectively inputting the geomorphic features andthe topographic features into a plurality of trained machine learning classification models to obtain a plurality of first classification results and a plurality of second classification results, andinputting the stratum features into the trained machine learning classification models to obtain a third classification result; and fusing the plurality of first classification results and the plurality of second classification results based on a D-S evidence theory, or fusing the plurality of first classification results, the plurality of second classification results and the third classificationresult to obtain a substrate classification result of the underwater multi-source acoustic image. According to the invention, automatic and high-precision classification of underwater substrates canbe realized.

Description

technical field [0001] The invention relates to the technical field of underwater acoustic image classification, in particular to an underwater multi-source acoustic image bottom classification method and system based on decision-making level fusion. Background technique [0002] Substrate classification refers to the classification of bottom materials (such as mud, sand, coarse sand, gravel, reef, etc.) of water bodies such as oceans, lakes, and rivers. The classification of underwater sediments is of great significance to underwater scientific research, resource development, environmental protection and underwater rights protection. Multi-source detection such as side-scan sonar, multi-beam sonar system and shallow strata profiler are commonly used techniques for acoustic sediment classification. By analyzing and interpreting the multi-source acoustic images obtained by the above three types of acoustic equipment, the sediment Classification of types. Multi-source acoust...

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