Image Classification of Submarine Volcanic Smog Map Based on Convolution Neural Network In order to meet the problem of smoke image classification in submarine volcanic scene, in this paper, depth convolution neural network (Deep Convolutional Neural Networks, DCNN) is used to classify smoke seafloor map and smokefree seafloor map under smallscale data set and limited computing power. Firstly, the data enhancement technology is used to expand the data set through angle rotation, horizontal flipping, random cutting and adding Gaussian noise, and then the depth convolution neural network is built for training. Finally, the recognition and classification is carried out according to the prediction image label of the classifier. The experimental results show that the classification accuracy of the proposed method is more than 91%.

ElevateNet: A Convolutional Neural Network for Estimating the Missing Dimension in 2D Underwater Sonar Images In this work we address the challenge of predicting the missing dimension (elevation angle) from 2D underwater sonar images. The high noise levels in these images, from phenomena such as nondiffuse reflections, frequently limits the usefulness of physical models. We thus propose the utilization of Convolutional Neural Networks (CNNs) as a powerful method to extract meaningful information without being misled by noisy data. We also introduce a selfsupervised method that uses the physics of the sonar sensor to train the network on real data without groundtruth elevation maps. Our method can produce accurate elevation angle estimates given only a single image. Finally, we demonstrate that our method produces more accurate 3D reconstructions than competing methods, both in simulation and on real data.