Abstract
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.
Highlights
Light reflected and scattered from a sea surface flooded with random surface waves and wave breakers is depended on the surface geometry
We have established a new sea surface image analysis technique for extracting sea surface roughness measurements from visible images based on a novel concept of sea surface random field can be represented by texture features
The algorithms presented in this paper include gray level co-occurrence matrix, gray level-gradient cooccurrence matrix, Tamura texture feature, autocorrelation function, edge frequency and fractional Brownian motion autocorrelation approaches
Summary
Light reflected and scattered from a sea surface flooded with random surface waves and wave breakers is depended on the surface geometry. One motivation of our research is to build up a capacity for ocean wave observations, measurements, 3D reconstructions, and property estimations based on wave features extracted from 2D visible images These approaches can be applied to analysis of other reflecting surfaces of transparent or opaque objects. Garrison et al [8] made use of the global positioning system (GPS) delay mapping receiver from the NASA Langley Research Center to generate post-correlation powerversus-delay measurements form GPS signals reflected from the ocean surface. Our research focuses mainly on three aspects: texture measurements from visible images, estimations of wind speed (or sea surface roughness) functional dependency on texture features based on the extracted data, and sensitivity of the methods through tests under four type noises.
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