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A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Environmental Information Institute, Navigation College, Dalian Maritime University, Dalian 116026, China
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ISPRS Int. J. Geo-Inf. 2019, 8(4), 160; https://doi.org/10.3390/ijgi8040160
Received: 17 February 2019 / Revised: 22 March 2019 / Accepted: 24 March 2019 / Published: 27 March 2019
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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. View Full-Text
Keywords: Convolutional Neural networks (CNN); band selection; oil film; classification Convolutional Neural networks (CNN); band selection; oil film; classification
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Liu, B.; Li, Y.; Li, G.; Liu, A. A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill. ISPRS Int. J. Geo-Inf. 2019, 8, 160.

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