Abstract
When it comes to feature retention in multi-scale representations of ocean flow fields, not all data points are equal. Therefore, this paper proposes a method of selecting data points based on their importance. First, an autocorrelation analysis is performed on flow speed and the rate of change in flow direction. Then, the magnitude of speed and variation in the rate of change in flow direction are classified. Feature regions are determined according to autocorrelation aggregation and classification analysis. Then, rough set theory and evidence theory are applied, using these results to determine the weights of different points. Finally, these weights are used to construct multi-scale representations of ocean flow fields, which effectively retain flow-field characteristics.
Highlights
With recent advancements in data acquisition technology, the range and resolution of ocean flow field data being collected have both increased significantly, leading to a large amount of data with dense vector points [1,2]
In order to construct improved multi-scale representations of ocean flow fields, this paper proposed a weight-based method for sampling data points
The weights of data points were assigned according to the classification area where the data points were located. These four factors were taken as four conditional attributes of the feature region, and rough set theory was used to calculate the support degree of each factor to the feature region
Summary
With recent advancements in data acquisition technology, the range and resolution of ocean flow field data being collected have both increased significantly, leading to a large amount of data with dense vector points [1,2]. The data points can be assigned different weights, which reflect their relative importance and information content, and this can greatly improve the quality of multi-scale flow field representations [3]. In order to satisfy the feature extraction of large vector fields and ensure the sampling accuracy of the data, this paper proposes a multi-scale representation method of ocean flow fields based on feature analysis. We performs an autocorrelation analysis and classification analysis based on rates of change in speed and direction across the data points in the flow field in order to calculate the weight of each data point with respect to each attribute and extracted feature area. As with the flow speed, each data region will be allocated a weight based on the aggregation of the rate at which the nearby flow changes direction.
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