Next Article in Journal
GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora
Next Article in Special Issue
Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology
Previous Article in Journal
A Precise Urban Component Management Method Based on the GeoSOT Grid Code and BIM
Previous Article in Special Issue
Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

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
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 160;
Received: 17 February 2019 / Revised: 22 March 2019 / Accepted: 24 March 2019 / Published: 27 March 2019
PDF [3262 KB, uploaded 27 March 2019]


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top