Research on Object-Oriented Decision Fusion for Oil Spill Detection on Sea Surface

Ocean oil spill is an emergency with great harm. Optical remote sensing is an important means to monitor oil spill on the sea surface. Due to the influence of cloud and weather and the limitation of satellite revisit period, only limited sample data can be obtained. In the case of limited samples, the ability of learning sample features using a single supervised classifier is limited, which can not meet the needs of accurately monitor oil spill. This paper takes GF-1 WFV oil spill image as data source, and uses four classical supervised classification algorithms to extract oil spill information. From the point of view of target recognition information fusion, the advantages of multiple supervised classification algorithms are integrated. Decision fusion algorithm is used to realize multi-source oil spill information fusion, so as to improve the detection accuracy of remote sensing oil spill.