Hyperspectral remote sensing technology has played a vital role in the identification and monitoring of oil spill events, and amount of spectral indices have been developed. In this paper, the applicability of six frequently-used indices is analyzed, and a combination of spectral indices in aids of support vector machine (SVM) algorithm is used to identify the oil slicks and corresponding thickness. The six spectral indices are spectral rotation (SR), spectral absorption depth (HI), band ratio of blue and green (BG), band ratio of BG and shortwave infrared index (BGN), 555nm and 645nm normalized by the blue band index (NB) and spectral slope (ND). The experimental study is conducted in the Gulf of Mexico oil spill zone, with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery captured in May 17, 2010. The results show that SR index is the best in all six indices, which can effectively distinguish the thickness of the oil slick and identify it from seawater; HI index and ND index can obviously distinguish oil slick thickness; BG, BGN and NB are more suitable to identify oil slick from seawater. With the comparison among different kernel functions of SVM, the classify accuracy show that the polynomial and RBF kernel functions have the best effect on the separation of oil slick thickness and the relatively pure seawater. The applicability of spectral indices of oil slick and the method of oil film thickness identification will in aids of oil/gas exploration and oil spill monitoring.

Date Published: 25 October 2016
PDF: 8 pages
Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101561Q (25 October 2016); doi: 10.1117/12.2247308

Published in SPIE Proceedings Vol. 10156:
Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology