Lu G.-HTsai W.-TJahne B.WU-TING TSAI2021-08-052021-08-0520191962892https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078285031&doi=10.1109%2fTGRS.2019.2920280&partnerID=40&md5=39a1171bc8fe1c4fdb6dbfd66a8f314chttps://scholars.lib.ntu.edu.tw/handle/123456789/576872An image processing method utilizing multi-dimensional empirical mode decomposition is developed to decompose wind-wave surface imageries attributed to various flow processes governing interfacial transport, including gravity waves, capillary ripples, Langmuir cells, and quasi-streamwise turbulent eddies. The decomposition and combination strategies are based on the characteristic length scales and directionalities of the signatures induced by the flow processes. The decomposed imagery thus provides quantification of the contribution partition to the interfacial signatures by individual flow processes. Analyses of infrared images taken in the wind wave facility Aeolotron at Heidelberg University reveal: quasi-streamwise eddies, which arise from the turbulent shear layer, dominate the contribution to interfacial signatures at low wind speeds. For nonbreaking waves at intermediate wind speeds, the contribution partitions to interfacial signatures by the four flow processes are of the same order of magnitude. For microscale breaking waves, the dominant contribution is attributed to boundary layer disruption in the wake of spilling breakers. For breaking waves at high wind speeds, transverse turbulent eddies, which induce similar surface signatures as those by capillary ripples, and streamwise vortices caused by breaking also contribute to the transport; the decomposed imageries are thus induced by multiple flow processes. ? 1980-2012 IEEE.Atmospheric thermodynamics; Boundary layers; Image processing; Infrared imaging; Processing; Thermography (imaging); Water waves; Wind; Characteristic length; Combination strategies; Dominant contributions; Empirical Mode Decomposition; Image processing - methods; Interfacial transport; Quantitative separation; Wind wave; Shear flow; boundary layer; breaking wave; decomposition analysis; gravity wave; image processing; imaging method; quantitative analysis; wind velocity; wind wavejournal article10.1109/TGRS.2019.29202802-s2.0-85078285031