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

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

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

Physics | Updated：2024-10-14

- Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network
- Journal of Oceanology and Limnology Vol. 42, Issue 3, Pages: 728-743(2024)
- Affiliations：
  
  1.College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
  2.First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
  3.Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China
- Author bio：
  
  yangjunfang@upc.edu.cn
- Funds：
- DOI：
  CLC：
- Received：27 June 2023，
  
  Online First：30 August 2023，
  
  Published：01 May 2024
- Accepted：
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ZHANG Ning,YANG Junfang,LIU Shanwei,et al.Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network[J].Journal of Oceanology and Limnology,2024,42(03):728-743.
DOI：

ZHANG Ning,YANG Junfang,LIU Shanwei,et al.Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network[J].Journal of Oceanology and Limnology,2024,42(03):728-743. DOI：

摘要

Abstract

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.

关键词

Keywords

references

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