Detection and classification of oil spills in Envisat Asar imagery using adaptive filter and Fuzzy logic
Abstract
Microwave remote sensing technology has been used effectively in the early detection and classification of oil spills on the sea. However, due to the inherent nature of radar backscatter the imagery produced by SAR systems is usually degraded by speckle noise (which is caused by random constructive and destructive interference from the multiple scattering returns that will occur within each pixel). Moreover, the detection and analysis of oil spills using SAR imagery are also influenced by meteorological conditions on the sea surface such as wind, fluctuations of sea waves, sea surface temperature, and rains, as well as the physico-chemical characteristics and duration of an oil spill. This article presents the results of study on application of adaptive filter and Fuzzy logic to detect and classify oil spills on the sea in Envisat Asar imagery. This method can be used effectively in the case of complex oil spills which are difficult to identify by other methods.
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