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.
Highlights
The majority of oil is transported by ships, greatly increasing the risk of oil spills
The spatial resolution of hyperspectral images has improved with the development of sensor technology, so these images can provide multidimensional characteristics for target recognition in environmental monitoring and enable the classification of oil film thickness
This study explores how machineand deep-learning methods are used in the recognition of ship oil spills and in the classification of oil film thickness in hyperspectral data
Summary
The majority of oil is transported by ships, greatly increasing the risk of oil spills. Remote sensing technology is widely used in oil spill monitoring and research because of its advantages in large-area imaging. Laser, and multispectral images, hyperspectral remote sensing images have the following advantages: a wide monitoring range, continuous and high-dimensional object spectrum information, and an anti-interference capability. They play an important role in environmental monitoring. The spatial resolution of hyperspectral images has improved with the development of sensor technology, so these images can provide multidimensional characteristics for target recognition in environmental monitoring and enable the classification of oil film thickness. This study explores how machineand deep-learning methods are used in the recognition of ship oil spills and in the classification of oil film thickness in hyperspectral data. A detailed comparison analysis of the classification results is conducted using SVM algorithms and the back propagation (BP) neural network (BP) neural network algorithms
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