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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.

hyperspectral imagespectral analysisdimensionality reductionmultiscale superpixel level group clustering framework(MSGCF)double-branch dual-attention(DBDA)

Ning ZHANG、Junfang YANG、Shanwei LIU、Yi MA、Jie ZHANG

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College of Oceanography and Space Informatics,China University of Petroleum,Qingdao 266580,China

First Institute of Oceanography,Ministry of Natural Resources,Qingdao 266061,China

Technology Innovation Center for Ocean Telemetry,Ministry of Natural Resources,Qingdao 266061,China

国家自然科学基金国家自然科学基金山东省自然科学基金中央高校基本科研业务费专项

42206177U1906217ZR2022QD07521CX06057A

2024

10.1007/s00343-023-3122-5

海洋湖沼学报(英文版)
中国海洋湖沼学会

海洋湖沼学报(英文版)

CSTPCD
影响因子：0.386
ISSN：2096-5508
年,卷(期)：2024.42(3)