Research on Hyperspectral Unmixing Oil Spill Monitoring

Can Cui, Ying Li, Hong-Ji Chen, Bing-Xin Liu, Jin Xu, Guan-Nan Li
2017 Proceedings of the 2nd 2016 International Conference on Sustainable Development (ICSD 2016)   unpublished
Hyperspectral unmixing is an important technique for hyperspectral image analysis. In this paper, we took Airborne Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery as dataset to monitor oil spills. The information of oil spills was retrieved through image preprocessing, minimum noise fraction (MNF) feature extraction, endmember extraction (pure pixel index (PPI), unsupervised orthogonal subspace projection (UOSP)) and fully constrained least squares (FCLS) abundance
more » ... abundance estimation. In the steps of endmember extraction, the experiment measured endmember spectra of oil and water were used as reference spectra. Then we compared the endmember spectra extracted in the image to the measured spectra by the spectral angle. At last the FCLS abundance estimation was carried on to evaluate the endmember extraction quality. The result demonstrates that the unsupervised OSP-FCLS model is better than supervised PPI-FCLS endmember extraction.
doi:10.2991/icsd-16.2017.40 fatcat:wkxhsbq3lfc6nkdhvuj2ts3n74