|Title||Roughness analysis of sea surface from visible images by texture|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Pan H.L, Gao P.L, Zhou H.C, Ma R.X, Yang J.S, Zhang X.|
|Type of Article||Article|
|Keywords||autocorrelation function; Computer Science; edge; Engineering; fractional Brownian motion; frequency; Gray; gray level-gradient co-occurrence; level co-occurrence matrices; matrices; Sea measurements; Sea surface roughness; tamura texture features; Telecommunications|
This paper presents a roughness analysis of sea surface from visible images by feature measurements of texture for the first time. The algorithms presented in this paper include six texture feature measurements of sea surface use gray level co-occurrence matrix, gray level-gradient co-occurrence matrix, Tamura texture feature, autocorrelation function, edge frequency and fractional Brownian motion autocorrelation. The empirical relationship between wind speeds (or sea surface roughness) and image texture roughness are estimated based on the extracted data. Our experiments have demonstrated that our texture methods and empirical relation between wind speeds and image texture roughness can potentially be used to analyze sea surface roughness from visible images.