Oil spill detection by a support vector machine based on polarization decomposition characteristics

Marine oil spills have caused major threats to marine environment over the past few years. The early detection of the oil spill is of great significance for the prevention and control of marine disasters. At present, remote sensing is one of the major approaches for monitoring the oil spill. Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy (H) and reflection entropy (A). The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill. The findings show that the information extracted from (1-A)×(1-H) and (1-H)×A parameters is relatively evident effects. The results of extraction of the oil spill information based on H×A parameter are relatively not good. The combination of the two has something to do with H and A values. In general, when H>0.7, A value is relatively small. Here, the extraction of the oil spill information using (1-A)×(1-H) and (1-H)×A parameters obtains evident effects. Whichever combined parameter is adopted, oil well data would cause certain false alarm to the extraction of the oil spill information. In particular the false alarm of the extracted oil spill information based on (1-A)×(1-H) is relatively high, while the false alarm based on (1-A)×H and (1-H)×A parameters is relatively small, but an image noise is relatively big. The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature.