Topology-oriented 3D ocean flow field feature classification and tracking algorithm

The tracking analysis of ocean feature phenomena exists many problems, such as incomplete topological structure information extraction and unclear time-varying law information display, etc. In this paper, a topology-oriented 3D ocean flow field feature classification and tracking algorithm is proposed to solve the problem of flow field feature tracking in different scales. The algorithm consists of three parts: Initially, the adaptive circular sampling space manner is optimized and improved to adapt to the extraction of flow field feature regions at different scales in view of the imprecise definition of traditional feature regions. Secondly, feature seed points were screened by setting information entropy threshold and denoised by template detection method. Eventually, combined with the eigenvalues of Jacobian matrix at critical points, the extracted two-dimensional feature regions are classified, and the continuous three-dimensional flow field features are visually tracked. By analyzing the experimental results of ocean flow field data of different depth and dimension, the validity and feasibility of topological feature structure classification and tracking algorithm are proved.