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Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology

1,2, 1,2,*, 1,2 and 1,2
1
Navigation College, Dalian Maritime University, Dalian 116026, China
2
Environmental Information Institute, Dalian Maritime University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 181; https://doi.org/10.3390/ijgi8040181
Received: 13 February 2019 / Revised: 16 March 2019 / Accepted: 31 March 2019 / Published: 6 April 2019
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Abstract

Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified. View Full-Text
Keywords: spectral information extraction; deep learning; oil film classification spectral information extraction; deep learning; oil film classification
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Zhu, X.; Li, Y.; Zhang, Q.; Liu, B. Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology. ISPRS Int. J. Geo-Inf. 2019, 8, 181.

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