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Oil slick extraction from hyperspectral images using a modified stacked auto-encoder network
Author(s): Wen Chang; Bingxin Liu; Qiang Zhang
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Paper Abstract

Hyperspectral remote sensing provides an outstanding tool in oil slick detection and classification, for its advantages in abundant spectral information. Many classification methods have been proposed and tested for oil spill extraction using hyperspectral images. However, the deep learning method were hardly researched to classify oil slicks using hyperspectral images. In this work, we proposed a spatial-spectral jointed Stacked Auto-encoder (SSAE) to extract and classify oil slicks on the sea surface. The traditional machine learning methods, Support Vector Machine (SVM), Back Propagation Neural network (BPNN) and Stacked Auto-encoder (SAE), were also adopted. The experimental results reveal that our proposed SSAE model can remarkably outperform the other models, especially for the thick oil films. The results of this work could provide an alternative method to extract oil slicks on hyperspectral remote sensing images.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117931 (14 August 2019); doi: 10.1117/12.2539664
Show Author Affiliations
Wen Chang, China Waterborne Transport Research Institute (China)
Bingxin Liu, Dalian Maritime Univ. (China)
Qiang Zhang, TAL Education Group (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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