Open AccessReference spectral signature selection using density-based cluster for automatic oil spill detection in hyperspectral images
Author(s) -
Delian Liu,
Jianqi Zhang,
Xiaorui Wang
Publication year - 2016
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.007411
Subject(s) - hyperspectral imaging , spectral signature , computer science , oil spill , signature (topology) , remote sensing , estimator , spectral bands , artificial intelligence , pattern recognition (psychology) , environmental science , mathematics , geology , statistics , environmental engineering , geometry
Reference spectral signature selection is a fundamental work for automatic oil spill detection. To address this issue, a new approach is proposed here, which employs the density-based cluster to select a specific spectral signature from a hyperspectral image. This paper first introduces the framework of oil spill detection from hyperspectral images, indicating that detecting oil spill requires a reference spectral signature of oil spill, parameters of background, and a target detection algorithm. Based on the framework, we give the new reference spectral signature selection approach in details. Then, we demonstrate the estimation of background parameters according to the reflectance of seawater in the infrared bands. Next, the conventional adaptive cosine estimator (ACE) algorithm is employed to achieve oil spill detection. Finally, the proposed approach is tested via several practical hyperspectral images that are collected during the Horizon Deep water oil spill. The experimental results show that this new approach can automatically select the reference spectral signature of oil spills from hyperspectral images and has high detection performance.