A method for seabed bottom sonar image classification based on convolution neural network

Abstract

The invention discloses a bottom material sonar image classification method based on a convolution neural network, belonging to the technical field of image classification. The method comprises obtaining the sonar image of seafloor bottom, preprocessing the image with denoising and enhancement, extracting the edge shape based on a canny algorithm, and generating a gray scale-basic element co-occurrence matrix, constructing a convolution neural network classifier structure and a sample set, training a neural network, obtaining classification model and realizing bottom material sonar image classification. The present invention is directed to the graphical characteristics of bottom material sonar images of seafloor, the disadvantage of using single method is solved, and the learning strategyof the convolution neural network classifier is used to learn and train different types of seabed sediment, and finally the classification model with classification function is obtained, and the purpose of fast and accurate classification of seabed sediment sonar images is achieved.

Description

technical field [0001] The invention belongs to the technical field of image classification, and in particular relates to a method for classifying sonar images of sea bottom quality based on convolutional neural networks. Background technique [0002] With the rapid development of sonar technology, sonar images of seabed bottom can contain relatively rich information about seabed topography and bottom characteristics. Protection is of great significance, and the classification of sonar images of seabed bottom has also become a research hotspot. However, limited by the underwater complex sound field environment and the performance of sonar equipment, sonar images have problems such as serious speckle noise interference, blurred edge features, low contrast, and uneven brightness. Issues that require urgent attention. [0003] Now widely used classification methods can be divided into two categories: unsupervised classification and supervised classification according to the c...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problems of weak generalization ability, long training time, and high requirements on the quality of training samples in the prior art in the classification of sea bottom sonar images, and propose a sea bottom based on convolutional neural network. Quality sonar image classification method, described method specifically comprises steps:

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