Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network.

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution. The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit, which will improve the timeliness of oil spill emergency monitoring. At the same time, the combination of spectral and spatial features can improve the accuracy of oil spill monitoring. Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions, for which the multiscale superpixel level group clustering framework (MSGCF) was used to select spectral feature bands with strong separability. In addition, the double-branch dual-attention (DBDA) model was applied to identify crude oil and its emulsions. Compared with the recognition results based on original hyperspectral images, using the feature bands determined by MSGCF improved the recognition accuracy, and greatly shortened the running time. Moreover, the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined, and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010. This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration, laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit. [ABSTRACT FROM AUTHOR]