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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]

Rising sea level is of great significance to coastal societies; predicting sea level extent in coastal regions is critical. When carrying out predictions, the subsequences obtained using decomposition methods may exhibit a certain regularity and therefore can provide multidimensional information that can be used to improve prediction models. Traditional decomposition methods such as seasonal and trend decomposition using Loess (STL) focus mostly on the fluctuating trend of time series and ignore its impact on prediction. Methods in the signal decomposition domain, such as variational mode decomposition (VMD), have no physical significance. In response to the above problems, a new decomposition method for sea level anomaly time series prediction (DMSLAP) is proposed. With this method, the trend term in a time series can be isolated and the effects of abnormal sea level change behaviors can be attenuated. We decompose multiperiod characteristics using this method while maintaining the smoothness of the analyzed series. Satellite altimetry data from 1993 to 2020 are used in experiments conducted in the study area. The results are then compared with predictions obtained using existing decomposition methods such as the STL and VMD methods and time varying filtering based on empirical mode decomposition (TVF-EMD). The performance of DMSLAP combined with a prediction method resulted in optimal sea level anomaly (SLA) predictions, with a minimum root mean square error (RMSE) of 1.40 cm and a maximum determination coefficient (R2) of 0.93 during 2020. The DMSLAP method was more accurate when predicting 1-year data and 3-year data. The TVF-EMD and DMSLAP methods had comparable accuracies, and the periodic term decomposed by the DMSLAP method was more in line with the actual law than that derived using the TVF-EMD method. Thus, DMSLAP can decompose SLA time series better than existing methods and is an effective tool for obtaining short-term SLA prediction. [ABSTRACT FROM AUTHOR]

The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U. prolifera patches, omission of tiny patches, and overestimation of coverage area. The decomposition of U. prolifera mixed pixel addresses the issue of coverage area overestimation, and the remaining problems can be alleviated by subpixel mapping (SPM). Due to the drift and dissipation of U. prolifera, a suitable SPM method is the single image-based unsupervised method. However, the method has difficulties in detail reconstruction, insufficient learning of spectral information, and SPM error introduced by abundance deviation. Therefore, we proposed a multiple-feature decision fusion SPM (MFDFSPM) method. It involves three branches to obtain the spatial, abundance, and spectral features of U. prolifera while considers multi-feature information using the fusion strategy. Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U. prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation, which produced subpixel map with more detailed spatial information and less noise. [ABSTRACT FROM AUTHOR]

Marine oil spills are among the most significant sources of marine pollution. Synthetic aperture radar (SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identification. However, speckle noise, weak boundaries, and intensity inhomogeneity often exist in the oil spill regions of SAR imagery, which will seriously affect the accurate identification of oil spills. To enhance marine oil spill segmentation of SAR images, a fast, edge-preserving framework based on the distance-regularized level set evolution (DRLSE) model was proposed. Specifically, a bilateral filter penalty term is designed and incorporated into the DRLSE energy function (BF-DRLSE) to preserve the edges of oil spills, and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods, the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy (97.83%). In addition, using an appropriate initial box boundary for the DRLSE method accelerated the global search process, improved the accuracy of oil spill segmentation, and reduced computational time. Therefore, the results suggest that the proposed framework is effective and applicable for marine oil spill segmentation. [ABSTRACT FROM AUTHOR]