ABSTRACT
This chapter critically evaluates the potential of synthetic aperture radar’s (SAR) and quantum machine learning for modeling the trajectory movement of oil spills. Unquestionably, SAR techniques are intensively used for monitoring and modeling the sea surface physical properties, for instance, wave spectra, current movements, and bathymetry mapping. The main question which can, of course, be raised is how SAR can simulate the trajectory movement of an oil spill from sequence data without involving nonlinearity of the Doppler frequency model. It is clear that the VV polarization provides excellent visualization for oil spill than HH polarization. This is demonstrated by both ENVISAT and CSK SAR data. In fact, VV polarization performs better at highlighting oil slicks from sea background for stronger power return, however, it may vary according to different oil types and sea conditions.
The multiSAR data with short revisit are able to track the trajectory movement of an oil spill while the quantum Hopfield algorithm is able to simulate and forecast the trajectory movements of the oil spills. An excellent example was the oil spill disaster of the Gulf of Mexico on 2010.
