Research On Feature Extraction Of Underwater Acoustic Image Based On Wavelet Moment

The underwater target classification and recognition technology relying on sonar equipment plays an increasingly important role in marine resource exploration,underwater fish identification,underwater salvage and other environments.The general steps of underwater target recognition are original sonar image acquisition,image preprocessing,image feature extraction,and target classification recognition.In the entire process of sonar image target recognition,each link has a decisive role.Among them,the feature extraction link is particularly important.The invariance of features,that is,the better the invariance of displacement,scale and rotation,and the stronger the anti-noise performance,the higher the classification and recognition ability.There has been a lot of work on the feature extraction algorithm based on wavelet invariant moments,but the predecessors basically applied it to optical images,acoustic images,especially underwater target recognition.For the specific scallop and starfish targets in this experiment,considering the defects of the sonar image with blurred contours and many noises,the Hu moment,Zernike moment and wavelet invariant moment are applied to this type of image.The main content and results include:(1)The basic concepts and principles of Hu moment,Zernike moment and wavelet invariant moment characteristics are introduced in detail,and the displacement,scale and rotation invariance of the algorithm are derived and proved.(2)Sonar image moment feature extraction experiment.Taking scallops as the research object,the original sonar image was transformed by displacement,scale and rotation,and Gaussian noise of different degrees was added.Use MATLAB to program and extract the eigenvalues under various moment algorithms,and analyze and compare the invariance and anti-noise performance of the features under different algorithms.(3)Starfish and scallop classification and recognition test based on BP neural network.Collect 3000 original sonar images,perform preprocessing and feature extraction on them,and select the feature values extracted by the three invariant moment algorithms respectively,and then input them into the neural network classifier to judge the three invariant moment algorithms through the recognition effect Performance.The innovations of this article are summarized as follows:(1)For the scallop and starfish sonar images in this paper,on the basis of the traditional global moment Hu moment and Zernike moment,a wavelet invariant moment with local characteristics and good noise resistance performance is proposed,and the wavelet is not verified by experimental values.Variable torque with strong adaptability.(2)In order to obtain better recognition results,a method based on feature fusion and feature dimensionality reduction is proposed.Experiments show that this method improves the overall recognition rate and reduces the calculation time.