Finding Water Depths from Synthetic Remotely Sensed Images

Mathematical morphology coupled with creation of a time stack image and Principal Oscillation Pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. The data consisted of 60 images, each 256x256 pixels, separated by 1 second in time. These images are produced by a simulator which creates a noise-free time series of images showing sea surface elevation. Simulated images allow testing processing and analysis methods to develop confidence when extended to actual images. Wave crests and a time stack image needed to measure speeds and wavelengths are obtained from mathematical morphology. The depth results are compared with those derived using principal oscillation pattern analysis by which three significant complex pairs of patterns are found with their corresponding characteristic times: e-folding times and periods. The wavelengths are derived through one-dimensional waves extracted from the principal oscillation patterns. From these results and by using the classical hydrodynamic theory of gravity waves, water depths are determined. Both analyses yield small errors for the simulated data, indicating both methods should perform reliably for real data. Corresponding author: Dr. Ma Yolanda Luna. Dpto. Astrofísica y CC. de la Atmósfera. Facultad CC. Físicas. Universidad Complutense de Madrid. 28040 Madrid, Spain. Topic area: Image sensing: analysis; feature extraction.