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 reali...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.展开更多
In response to the limitations and low computational efficiency of one-dimensional water and sediment models in representing complex hydrological conditions, this paper proposes a dual branch convolution method based ...In response to the limitations and low computational efficiency of one-dimensional water and sediment models in representing complex hydrological conditions, this paper proposes a dual branch convolution method based on deep learning. This method utilizes the ability of deep learning to extract data features and introduces a dual branch convolutional network to handle the non-stationary and nonlinear characteristics of noise and reservoir sediment transport data. This method combines permutation variant structure to preserve the original time series information, constructs a corresponding time series model, models and analyzes the changes in the outbound water and sediment sequence, and can more accurately predict the future trend of outbound sediment changes based on the current sequence changes. The experimental results show that the DCON model established in this paper has good predictive performance in monthly, bimonthly, seasonal, and semi-annual predictions, with determination coefficients of 0.891, 0.898, 0.921, and 0.931, respectively. The results can provide more reference schemes for personnel formulating reservoir scheduling plans. Although this study has shown good applicability in predicting sediment discharge, it has not been able to make timely predictions for some non-periodic events in reservoirs. Therefore, future research will gradually incorporate monitoring devices to obtain more comprehensive data, in order to further validate and expand the conclusions of this study.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.42206177,U1906217)the Shandong Provincial Natural Science Foundation(No.ZR2022QD075)the Fundamental Research Funds for the Central Universities(No.21CX06057A)。
文摘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.
基金NationalNatural Science Foundation of China(U2243236,51879115,U2243215),Recipients of funds:Xinjie Li,URL:https://www.nsfc.gov.cn/(accessed on 25 November 2024).
文摘In response to the limitations and low computational efficiency of one-dimensional water and sediment models in representing complex hydrological conditions, this paper proposes a dual branch convolution method based on deep learning. This method utilizes the ability of deep learning to extract data features and introduces a dual branch convolutional network to handle the non-stationary and nonlinear characteristics of noise and reservoir sediment transport data. This method combines permutation variant structure to preserve the original time series information, constructs a corresponding time series model, models and analyzes the changes in the outbound water and sediment sequence, and can more accurately predict the future trend of outbound sediment changes based on the current sequence changes. The experimental results show that the DCON model established in this paper has good predictive performance in monthly, bimonthly, seasonal, and semi-annual predictions, with determination coefficients of 0.891, 0.898, 0.921, and 0.931, respectively. The results can provide more reference schemes for personnel formulating reservoir scheduling plans. Although this study has shown good applicability in predicting sediment discharge, it has not been able to make timely predictions for some non-periodic events in reservoirs. Therefore, future research will gradually incorporate monitoring devices to obtain more comprehensive data, in order to further validate and expand the conclusions of this study.
