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

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.

[1]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[2]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[3]  P. Fisher.  The pixel: A snare and a delusion , 1997 .

[4]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  B. Hörig,et al.  Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons , 2004 .

[7]  Chris H. Q. Ding,et al.  Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..

[8]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Camilla Brekke,et al.  Oil Spill Detection in Radarsat and Envisat SAR Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Luciano Alparone,et al.  Signal-dependent noise modelling and estimation of new-generation imaging spectrometers , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[11]  Qing-jiu Tian,et al.  [Spectral response analysis of offshore thin oil slicks]. , 2009, Guang pu xue yu guang pu fen xi = Guang pu.

[12]  Giles M. Foody,et al.  Feature Selection for Classification of Hyperspectral Data by SVM , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Raymond F. Kokaly,et al.  A method for quantitative mapping of thick oil spills using imaging spectroscopy , 2010 .

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  Ira Leifer,et al.  Magnitude and oxidation potential of hydrocarbon gases released from the BP oil well blowout , 2011 .

[16]  Cathleen E. Jones,et al.  State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill , 2012 .

[17]  Jan Svejkovsky,et al.  Operational Utilization of Aerial Multispectral Remote Sensing during Oil Spill Response , 2012 .

[18]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[19]  T. Mudge,et al.  Characterization of oil slicks at sea using remote sensing techniques , 2012, 2012 Oceans.

[20]  Antonio J. Plaza,et al.  Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[21]  T. Alves,et al.  A three-step model to assess shoreline and offshore susceptibility to oil spills: the South Aegean (Crete) as an analogue for confined marine basins. , 2014, Marine pollution bulletin.

[22]  M. Fingas,et al.  Review of oil spill remote sensing. , 2014, Marine pollution bulletin.

[23]  Fan Zhang,et al.  Deep Convolutional Neural Networks for Hyperspectral Image Classification , 2015, J. Sensors.

[24]  Nikolaos Doulamis,et al.  Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[25]  Xiuping Jia,et al.  Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Shihong Du,et al.  Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[27]  T. Alves,et al.  Hindcast, GIS and susceptibility modelling to assist oil spill clean-up and mitigation on the southern coast of Cyprus (Eastern Mediterranean) , 2016 .

[28]  Ying Li,et al.  Assessing Sensitivity of Hyperspectral Sensor to Detect Oils with Sea Ice , 2016 .

[29]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[30]  Qi Li,et al.  Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features , 2016, Remote. Sens..

[31]  Peng Chen,et al.  Extraction of Oil Spill Information Using Decision Tree Based Minimum Noise Fraction Transform , 2016, Journal of the Indian Society of Remote Sensing.

[32]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[33]  Qian Du,et al.  Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Jun Li,et al.  Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.

[35]  Jubai An,et al.  Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN , 2017, Sensors.

[36]  Lei Guo,et al.  Dimension Reduction Aided Hyperspectral Image Classification with a Small-sized Training Dataset: Experimental Comparisons , 2017, Sensors.

[37]  Naif Alajlan,et al.  Deep Learning Approach for Car Detection in UAV Imagery , 2017, Remote. Sens..

[38]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[39]  Matthew L. Clark,et al.  One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California , 2017, Remote. Sens..

[40]  Ying Li,et al.  Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network , 2017, Remote. Sens..

[41]  Yiming Yan,et al.  Spectral–spatial classification of hyperspectral images using trilateral filter and stacked sparse autoencoder , 2017 .

[42]  Derek T. Anderson,et al.  Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .

[43]  Ying Li,et al.  A New Endmember Preprocessing Method for the Hyperspectral Unmixing of Imagery Containing Marine Oil Spills , 2017, ISPRS Int. J. Geo Inf..

[44]  Elena Marchiori,et al.  Convolutional Neural Networks and Data Augmentation for Spectral-Spatial Classification of Hyperspectral Images , 2017, ArXiv.

[45]  Guangmin Sun,et al.  Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images , 2017 .

[46]  Carl E. Brown,et al.  A Review of Oil Spill Remote Sensing , 2017, Sensors.

[47]  A. Vetrivel,et al.  Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[48]  Ying Li,et al.  Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction , 2018, Sensors.

[49]  Antonio-Javier Gallego,et al.  Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Elena Marchiori,et al.  Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting , 2018, Remote. Sens..

[51]  Antonio Plaza,et al.  A new deep convolutional neural network for fast hyperspectral image classification , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[52]  Xinwen Cheng,et al.  Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images , 2018, Remote. Sens..

[53]  José Cristóbal Riquelme Santos,et al.  A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks , 2019, Remote. Sens..