Oil spill thickness estimation using unmixing methods

Sykas, D; Karathanassi, V; Andreou, Ch; Kolokoussis, P

dc.contributor.author	Sykas, D	en
dc.contributor.author	Karathanassi, V	en
dc.contributor.author	Andreou, Ch	en
dc.contributor.author	Kolokoussis, P	en
dc.date.accessioned	2014-03-01T02:53:23Z
dc.date.available	2014-03-01T02:53:23Z
dc.date.issued	2011	en
dc.identifier.issn	21586276	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36288
dc.subject	abundance fraction	en
dc.subject	oil spills	en
dc.subject	thickness	en
dc.subject	unmixing methods	en
dc.subject.other	abundance fraction	en
dc.subject.other	Artificial image	en
dc.subject.other	Constrained Energy Minimization	en
dc.subject.other	Constrained least squares	en
dc.subject.other	Correlation function	en
dc.subject.other	Endmembers	en
dc.subject.other	Hyperspectral Data	en
dc.subject.other	Image reconstruction techniques	en
dc.subject.other	Laboratory measurements	en
dc.subject.other	Logarithmic equations	en
dc.subject.other	Network-based	en
dc.subject.other	Orthogonal subspace projection	en
dc.subject.other	Spectral signature	en
dc.subject.other	Spectral unmixing	en
dc.subject.other	thickness	en
dc.subject.other	Thickness estimation	en
dc.subject.other	Unmixing	en
dc.subject.other	Water surface	en
dc.subject.other	Estimation	en
dc.subject.other	Fluorine	en
dc.subject.other	Image reconstruction	en
dc.subject.other	Oil spills	en
dc.subject.other	Remote sensing	en
dc.subject.other	Signal processing	en
dc.subject.other	Thickness measurement	en
dc.subject.other	Least squares approximations	en
dc.title	Oil spill thickness estimation using unmixing methods	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/WHISPERS.2011.6080935	en
heal.identifier.secondary	6080935	en
heal.identifier.secondary	http://dx.doi.org/10.1109/WHISPERS.2011.6080935	en
heal.publicationDate	2011	en
heal.abstract	The paper presents a new method for estimating oil spill thickness using hyperspectral data. The method relies on the abundance fractions provided by spectral unmixing methods. Given that the materials which compose the spectral signature of pixels presenting oil spills are oil and water, correlation functions between abundance fractions of these endmembers and thickness of oil spills were established using artificial images. The artificial images were created using laboratory measurements of oil spills. Thirteen different types of oil were used for the production of the relevant artificial images, each one presenting oil spread on water surface with six different levels of thickness. Unmixing was performed with the Fully constrained Network Based Method (F-NBM), Fully Constrained Least Square method (FCLS), Orthogonal Subspace Projection (OSP) and Constrained Energy Minimization (CEM). The unmixing results were evaluated using image reconstruction techniques. F-NBM produced the most reliable abundances. Logarithmic equations provided the most reliable oil thickness estimations for the examined oil types. Oil thickness can satisfactorily be estimated using the abundance value of water, without requiring the knowledge of the oil type. © 2011 IEEE.	en
heal.journalName	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing	en
dc.identifier.doi	10.1109/WHISPERS.2011.6080935	en