Side-scan sonar (SSS) images are difficult for automatic analysis due to the acoustic measurement parameters as well as the number of different objects that can be distant. In addition, there is a risk that the seabed analysis application may be attacked. For this purpose, we propose a solution based on convolutional neural networks with bilinear pooling in order to achieve higher values of classification accuracy. Bilinear pooling merge data from two networks and return classification results. The first network’s branch receives the original image and the second one after applying the superpixel method. This approach allows to focus on different types of features. In addition, we introduced a mechanism of poisoning detection that analyze images and results from the network. For the evaluation process, we used the real SSS images obtained between two water channels in Szczecin city in north-western Poland. The importance of scientific research indicates the accuracy of the analysis as well as the safety of the measurements performed.

Analysis of the seabed using sonar is a key technology enabling the assessment of the substrate, detection and classification of objects located there. However, quite often sonar data is processed by users due to the small amount of measurement data. This is due to the need to create large data sets, and creating a sonar image is often dependent on atmospheric conditions. In this paper, we present a solution based on digital twins that allows the implementation of a digital twin layer for sonar applications. A digital twin layer based on generative and classification network models increases the amount of data and improves the effectiveness of solutions. For this purpose, we propose multi-attention models that focus on local and global sonar features and enable their fusion. Moreover, a technique for exchanging weights between networks in such a solution was modeled to reduce the amount of computing power. The proposed approach allows for analyzing images by focusing on different features and increasing the automatization of processing its data. To verify the operation, various sonar data were used and high classification accuracy was achieved as well as the generation of new data.

Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks.