The technologies of sonar and laser scanning are an efficient and widely used source of spatial information with regards to underwater and over ground environment respectively. The measurement data are usually available in the form of groups of separate points located irregularly in three-dimensional space, known as point clouds. This data model has known disadvantages, therefore in many applications a different form of representation, i.e. 3D surfaces composed of edges and facets, is preferred with respect to the terrain or seabed surface relief as well as various objects shape. In the paper, the authors propose a new approach to 3D shape reconstruction from both multibeam and LiDAR measurements. It is based on a multiple-step and to some extent adaptive process, in which the chosen set and sequence of particular stages may depend on a current type and characteristic features of the processed data. The processing scheme includes: 1) pre-processing which may include noise reduction, rasterization and pre-classification, 2) detection and separation of objects for dedicated processing (e.g. steep walls, masts), and 3) surface reconstruction in 3D by point cloud triangulation and with the aid of several dedicated procedures. The benefits of using the proposed methods, including algorithms for detecting various features and improving the regularity of the data structure, are presented and discussed. Several different shape reconstruction algorithms were tested in combination with the proposed data processing methods and the strengths and weaknesses of each algorithm were highlighted.

The approach to seabed classification based on processing multibeam sonar echoes is presented. The multibeam sonars, besides their well verified and widely used applications like high resolution bathymetry measurements or underwater object imaging, are also the promising tool in seafloor identification and classification, having several advantages over conventional single beam echosounders. The proposed seabed classification method assumes calculation of a set of parameters of an echo envelope, similarly as in single beam classification. These parameters include echo energy, echo length, statistical moments of echo energy and the set of echo shape descriptors. They are extracted for each consecutive beam allowing the estimation of their dependence on seafloor incident angle. The characteristic features of this dependence are described quantitatively and constitute the input information for an automatic supervised seabed classifier. The results of the simple classification procedure applied for multibeam data records acquired from several bottom types in Gdańsk Bay region are presented and discussed.

The results of the combined approach using fractal analysis and inverse filtering methods applied to sea‐bottom‐type recognition are presented. The single‐beam one‐frequency echosounder was used in described investigations. The bottom‐scattering impulse response was used to extract descriptive parameters of a seabed type. Both direct methods, including windowed SVD, regularization and wavelet analysis, as well as iterative techniques like maximum entropy, and expectation, maximization, and smoothing (EMS) were investigated. The data were acquired from acoustics surveys on Lake Washington and Liberty Bay. The results were compared and discussed especially in the context of excessive smoothing of the extracted fractal features by applied newly developed inverse techniques.