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
- Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network
- 海洋湖沼学报(英文) 2024年42卷第3期页码：728-743
- 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：
  
  the National Natural Science Foundation of China(42206177;U1906217);the Shandong Provincial Natural Science Foundation(ZR2022QD075);the Fundamental Research Funds for the Central Universities(21CX06057A)
- DOI：
  中图分类号：
- 收稿：2023-06-27，
  
  网络首发：2023-08-30，
  
  纸质出版：2024-05-01
- Accepted：
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Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network[J]. 海洋湖沼学报(英文), 2024,42(3):728-743.

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.
Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network[J]. 海洋湖沼学报(英文), 2024,42(3):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

Angelliaume S , Boisot O , Guérin C A . 2018a . Dual-polarized L-band SAR imagery for temporal monitoring of marine oil slick concentration . Remote Sensing , 10 ( 7 ): 1012 , https://doi.org/10.3390/rs10071012 https://doi.org/10.3390/rs10071012 .

Angelliaume S , Dubois-Fernandez P C , Jones C E et al . 2018b . SAR imagery for detecting sea surface slicks: performance assessment of polarization-dependent parameters . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 8 ): 4237 - 4257 , https://doi.org/10.1109/TGRS.2018.2803216 https://doi.org/10.1109/TGRS.2018.2803216 .

Brekke C , Solberg A H S . 2005 . Oil spill detection by satellite remote sensing . Remote Sensing of Environment , 95 ( 1 ): 1 - 13 , https://doi.org/10.1016/j.rse.2004.11.015 https://doi.org/10.1016/j.rse.2004.11.015 .

Cai Y M , Liu X B , Cai Z H . 2020 . BS-Nets: an end-to-end framework for band selection of hyperspectral image . IEEE Transactions on Geoscience and Remote Sensing , 58 ( 3 ): 1969 - 1984 , https://doi.org/10.1109/TGRS.2019.2951433 https://doi.org/10.1109/TGRS.2019.2951433 .

Clark R N , Swayze G A , Leifer I et al . 2010 . A method for quantitative mapping of thick oil spills using imaging spectroscopy . U.S. Geological Survey , https://doi.org/10.3133/ofr20101167 https://doi.org/10.3133/ofr20101167 .

Dong Y Z , Liu Y X , Hu C M et al . 2022 . Chronic oiling in global oceans . Science , 376 ( 6599 ): 1300 - 1304 , https://doi.org/10.1126/science.abm5940 https://doi.org/10.1126/science.abm5940 .

Du K , Ma Y , Jiang Z C et al . 2021 . Detection of oil spill in the sun glint region of optical remote sensing data based on the convolutional neural network method . Marine Sciences , 45 ( 4 ): 22 - 30 , https://doi.org/10.11759/hykx20200105001. https://doi.org/10.11759/hykx20200105001. (in Chinese with English abstract)

Duan P H , Xie Z J , Kang X D et al . 2022 . Self-supervised learning-based oil spill detection of hyperspectral images . Science China Technological Sciences , 65 ( 4 ): 793 - 801 , https://doi.org/10.1007/s11431-021-1989-9 https://doi.org/10.1007/s11431-021-1989-9 .

Fingas M . 1995 . Water-in-oil emulsion formation: a review of physics and mathematical modelling . Spill Science & Technology Bulletin , 2 ( 1 ): 55 - 59 , https://doi.org/10.1016/1353-2561(95)94483-Z https://doi.org/10.1016/1353-2561(95)94483-Z .

Girard-Ardhuin F , Mercier G , Collard F et al . 2005 . Operational oil-slick characterization by SAR imagery and synergistic data . IEEE Journal of Oceanic Engineering , 30 ( 3 ): 487 - 495 , https://doi.org/10.1109/JOE.2005.857526 https://doi.org/10.1109/JOE.2005.857526 .

Guo J , Meng J M , He Y J . 2016 . Scattering model research based on two-dimensional laser observation of spilled oil and emulsification processes . Marine Sciences , 40 ( 2 ): 159 - 164 , https://doi.org/10.11759/hykx20150927002. https://doi.org/10.11759/hykx20150927002. (in Chinese with English abstract)

Hu C M , Lu Y C , Sun S J et al . 2021 . Optical remote sensing of oil spills in the ocean: what is really possible? Journal of Remote Sensing , 2021 : 9141902 , https://doi.org/10.34133/2021/9141902 https://doi.org/10.34133/2021/9141902 .

Jha M N , Levy J , Gao Y . 2008 . Advances in remote sensing for oil spill disaster management: state-of-the-art sensors technology for oil spill surveillance . Sensors , 8 ( 1 ): 236 - 255 , https://doi.org/10.3390/s8010236 https://doi.org/10.3390/s8010236 .

Jia S , Yuan Y , Li N Y et al . 2022 . A multiscale superpixel-level group clustering framework for hyperspectral band selection . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5523418 , https://doi.org/10.1109/TGRS.2022.3150361 https://doi.org/10.1109/TGRS.2022.3150361 .

Jiang Z C , Zhang J , Ma Y et al . 2021 . Hyperspectral remote sensing detection of marine oil spills using an adaptive long-term moment estimation optimizer . Remote Sensing , 14 ( 1 ): 157 , https://doi.org/10.3390/rs14010157 https://doi.org/10.3390/rs14010157 .

Kang X D , Deng B , Duan P H et al . 2023 . Self-supervised spectral-spatial transformer network for hyperspectral oil spill mapping . IEEE Transactions on Geoscience and Remote Sensing , 61 : 5507410 , https://doi.org/10.1109/TGRS.2023.3260987 https://doi.org/10.1109/TGRS.2023.3260987 .

Li G N , Li Y , Hou Y C et al . 2021a . Marine oil slick detection using improved polarimetric feature parameters based on polarimetric synthetic aperture radar data . Remote Sensing , 13 ( 9 ): 1607 , https://doi.org/10.3390/rs13091607 https://doi.org/10.3390/rs13091607 .

Li H M . 2013 . Numerical Simulation of the Spread-Diffusion Process of Oil Released from Seabed in Penglai 19 - 3 AreaOilfield. Ocean University of China, Qingdao, China. p . 1 - 82 , https://doi.org/10.7666/d.D327054. https://doi.org/10.7666/d.D327054. (in Chinese with English abstract)

Li R , Zheng S Y , Duan C X et al . 2020 . Classification of hyperspectral image based on double-branch dual-attention mechanism network . Remote Sensing , 12 ( 3 ): 582 , https://doi.org/10.3390/rs12030582 https://doi.org/10.3390/rs12030582 .

Li Y , Yu Q L , Xie M et al . 2021b . Identifying oil spill types based on remotely sensed reflectance spectra and multiple machine learning algorithms . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 9071 - 9078 , https://doi.org/10.1109/JSTARS.2021.3109951 https://doi.org/10.1109/JSTARS.2021.3109951 .

Liu B X , Zhang Q , Li Y et al . 2019 . Spatial-spectral jointed stacked auto-encoder-based deep learning for oil slick extraction from hyperspectral images . Journal of the Indian Society of Remote Sensing , 47 ( 12 ): 1989 - 1997 , https://doi.org/10.1007/s12524-019-01045-y https://doi.org/10.1007/s12524-019-01045-y .

Lu Y C , Liu J Q , Ding J et al . 2019a . Optical remote identification of spilled oils from the SANCHI oil tanker collision in the East China Sea . Chinese Science Bulletin , 64 ( 31 ): 3213 - 3222 , http://engine.scichina.com/doi/10.1360/N972019-00094. http://engine.scichina.com/doi/10.1360/N972019-00094. (in Chinese with English abstract)

Lu Y C , Shi J , Hu C M et al . 2020 . Optical interpretation of oil emulsions in the ocean—Part II: applications to multi-band coarse-resolution imagery . Remote Sensing of Environment , 242 : 111778 , https://doi.org/10.1016/j.rse.2020.111778 https://doi.org/10.1016/j.rse.2020.111778 .

Lu Y C , Shi J , Wen Y S et al . 2019b . Optical interpretation of oil emulsions in the ocean—Part I: laboratory measurements and proof-of-concept with AVIRIS observations . Remote Sensing of Environment , 230 : 111183 , https://doi.org/10.1016/j.rse.2019.05.002 https://doi.org/10.1016/j.rse.2019.05.002 .

Luo F L , Zhang L P , Du B et al . 2020 . Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification . IEEE Transactions on Geoscience and Remote Sensing , 58 ( 8 ): 5336 - 5353 , https://doi.org/10.1109/TGRS.2020.2963848 https://doi.org/10.1109/TGRS.2020.2963848 .

Luo F L , Zou Z H , Liu J M et al . 2022 . Dimensionality reduction and classification of hyperspectral image via multistructure unified discriminative embedding . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5517916 , https://doi.org/10.1109/TGRS.2021.3128764 https://doi.org/10.1109/TGRS.2021.3128764 .

Mendelssohn I A , Andersen G L , Baltz D M et al . 2012 . Oil impacts on coastal wetlands: implications for the Mississippi River Delta ecosystem after the Deepwater Horizon oil spill . Bioscience , 62 ( 6 ): 562 - 574 , https://doi.org/10.1525/bio.2012.62.6.7 https://doi.org/10.1525/bio.2012.62.6.7 .

Nixon Z , Zengel S , Baker M et al . 2016 . Shoreline oiling from the Deepwater Horizon oil spill . Marine Pollution Bulletin , 107 ( 1 ): 170 - 178 , https://doi.org/10.1016/j.marpolbul.2016.04.003 https://doi.org/10.1016/j.marpolbul.2016.04.003 .

Ren G B , Guo J , Ma Y et al . 2019 . Oil spill detection and slick thickness measurement via UAV hyperspectral imaging . Haiyang Xuebao , 41 ( 5 ): 146 - 158 , https://doi.org/10.3969/j.issn.0253-4193.2019.05.015. https://doi.org/10.3969/j.issn.0253-4193.2019.05.015. (in Chinese with English abstract)

Shi J . 2019 . Study on Hyperspectral Remote Sensing of Marine Oil Emulsions. Nanjing University, Nanjing, China . p . 1 - 76 , https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201902&filename=1019116637.nh. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201902&filename=1019116637.nh. (in Chinese with English abstract)

Shi J , Jiao J N , Lu Y C et al . 2018 . Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer . ISPRS Journal of Photogrammetry and Remote Sensing , 146 : 251 - 259 , https://doi.org/10.1016/j.isprsjprs.2018.09.017 https://doi.org/10.1016/j.isprsjprs.2018.09.017 .

Skrunes S , Brekke C , Eltoft T . 2014 . Characterization of marine surface slicks by Radarsat-2 multipolarization features . IEEE Transactions on Geoscience and Remote Sensing , 52 ( 9 ): 5302 - 5319 , https://doi.org/10.1109/TGRS.2013.2287916 https://doi.org/10.1109/TGRS.2013.2287916 .

Song D M , Liu B , Li X et al . 2015 . Hyperspectral data spectrum and texture band selection based on the subspace-rough set method . International Journal of Remote Sensing , 36 ( 8 ): 2113 - 2128 , https://doi.org/10.1080/01431161.2015.1034892 https://doi.org/10.1080/01431161.2015.1034892 .

Sun S J , Hu C M . 2019 . The challenges of interpreting oil-water spatial and spectral contrasts for the estimation of oil thickness: examples from satellite and airborne measurements of the deepwater horizon oil spill . IEEE Transactions on Geoscience and Remote Sensing , 57 ( 5 ): 2643 - 2658 , https://doi.org/10.1109/TGRS.2018.2876091 https://doi.org/10.1109/TGRS.2018.2876091 .

Wang B , Shao Q F , Song D M et al . 2021 . A spectral-spatial features integrated network for hyperspectral detection of marine oil spill . Remote Sensing , 13 ( 8 ): 1568 , https://doi.org/10.3390/rs13081568 https://doi.org/10.3390/rs13081568 .

Wang Q , Zhang F H , Li X L . 2020 . Hyperspectral band selection via optimal neighborhood reconstruction . IEEE Transactions on Geoscience and Remote Sensing , 58 ( 12 ): 8465 - 8476 , https://doi.org/10.1109/TGRS.2020.2987955 https://doi.org/10.1109/TGRS.2020.2987955 .

Yang J F , Hu Y B , Zhang J et al . 2023 . Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing . Frontiers in Marine Science , 10 : 1135356 , https://doi.org/10.3389/fmars.2023.1135356 https://doi.org/10.3389/fmars.2023.1135356 .

Yang J F , Ma Y , Hu Y B et al . 2022 . Decision fusion of deep learning and shallow learning for marine oil spill detection . Remote Sensing , 14 ( 3 ): 666 , https://doi.org/10.3390/rs14030666 https://doi.org/10.3390/rs14030666 .

Yang J F , Wan J H , Ma Y et al . 2019 . Oil spill hyperspectral remote sensing detection based on DCNN with multi-scale features . Journal of Coastal Research , 90 (SI): 332 - 339 , https://doi.org/10.2112/SI90-042.1 https://doi.org/10.2112/SI90-042.1 .

Yang J F , Wan J H , Ma Y et al . 2020 . Characterization analysis and identification of common marine oil spill types using hyperspectral remote sensing . International Journal of Remote Sensing , 41 ( 18 ): 7163 - 7185 , https://doi.org/10.1080/01431161.2020.1754496 https://doi.org/10.1080/01431161.2020.1754496 .

Yang J F , Wan J H , Ma Y et al . 2021 . Accuracy assessments of hyperspectral characteristic waveband for common marine oil spill types identification . Marine Sciences , 45 ( 4 ): 97 - 105 , https://doi.org/10.11759/hykx20200720003. https://doi.org/10.11759/hykx20200720003. (in Chinese with English abstract)

Yekeen S T , Balogun A L . 2020 . Advances in remote sensing technology, machine learning and deep learning for marine oil spill detection, prediction and vulnerability assessment . Remote Sensing , 12 ( 20 ): 3416 , https://doi.org/10.3390/rs12203416 https://doi.org/10.3390/rs12203416 .

Zhong Z X , You F Q . 2011 . Oil spill response planning with consideration of physicochemical evolution of the oil slick: a multiobjective optimization approach . Computers & Chemical Engineering , 35 ( 8 ): 1614 - 1630 , https://doi.org/10.1016/j.compchemeng.2011.01.009 https://doi.org/10.1016/j.compchemeng.2011.01.009 .

Zhu X Y , Li Y , Zhang Q et al . 2019 . Oil film classification using deep learning-based hyperspectral remote sensing technology . ISPRS International Journal of Geo-Information , 8 ( 4 ): 181 , https://doi.org/10.3390/ijgi8040181 https://doi.org/10.3390/ijgi8040181 .

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