Abstract
Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.
Highlights
The ocean is an important part of the earth’s surface, accounting for approximately 71% of the total surface area, and serves as an indispensable component of the Earth’s ecosystem
We proposed a band selection based 1D convolutional neural networks (CNN) method to perform oil film identification and thickness classification over airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral images
We selected some bands with the main spectral features using spectral indices and minimum Redundancy Maximum Relevance (mRMR)
Summary
The ocean is an important part of the earth’s surface, accounting for approximately 71% of the total surface area, and serves as an indispensable component of the Earth’s ecosystem. Sudden oil spill accidents have become more frequent with increasing maritime traffic. These accidents include oil pipeline ruptures, oil and gas leakages, vessel collisions, illegal dumping, and blowouts, causing serious damage to the marine environment and ecological resources [1,2,3]. Compared with the traditional direct detection method that requires human control, satellite remote sensing technology can enable large-area monitoring of the spread, thickness, and type of an oil spill. These data compensate for the shortcomings of traditional direct surveillance methods and can guide surveillance aircraft and ships to conduct real-time monitoring of the most important parts of a spill. Remote sensing technology has become an essential tool for detecting oil spills [1,6,7]
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