Video Measurements and Analysis of Surface Gravity Waves in Shallow Water

This paper discusses a shallow-water wave height measurement method that uses high definition video cameras to image a water surface wave patch. Wave height time series are extracted from water surface video sequences. Wave features such as the wavelength distribution and energy contained in a wave patch (W/m2) were obtained by analyzing the extracted wave height time series and expressing the wind-driven wave energy as a wave energy spectrum. A Weibull probability distribution was used as the mathematical form of the energy spectrum. Wave spectra are used as input to a wave patch simulation model that generates simulated wind-driven wave images. The measurement protocol is inexpensive, easy to implement, and useful to calibrate and validate wind-driven wave models. The protocol is used to understand resuspension of bottom muds due to wind waves in shallow waters. Scaled staff gauges made of polyvinyl chloride (PVC) materials are placed in shallow water and imaged at 30 Hz followed by frame based image analysis to extract wave height time series. Wave spectra calculated using the fast Fourier transform (FFT) results in a Weibull probability distribution function (WPDF) energy spectrum. The estimated wave spectrum is used to estimate wave energy in W/m2 followed by generation of wave patch simulations of the water surface. Simulated wave patches are compared with the sensor-based wave patch video measurements. Sensitivity analysis of coefficients α and β in the model are used to adjust the synthetic wave images to measured wave patch images. The approach allows one to obtain an estimate of the energy (W/m2) transferred from the local wind field to a water surface gravity wave patch

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve \u3e90% accuracy on the detection of seagrass scars