Independent component analysis for coastal water mapping using hyperspectral datasets

Vassilia, K; Polychronis, K; Styliani, I

dc.contributor.author	Vassilia, K	en
dc.contributor.author	Polychronis, K	en
dc.contributor.author	Styliani, I	en
dc.date.accessioned	2014-03-01T02:46:12Z
dc.date.available	2014-03-01T02:46:12Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32598
dc.subject	Blind Source Separation	en
dc.subject	Case Study	en
dc.subject	Coastal Waters	en
dc.subject	Dimensional Reduction	en
dc.subject	Discrete Wavelet Transform	en
dc.subject	hyperspectral data	en
dc.subject	In Situ Measurement	en
dc.subject	Independent Component Analysis	en
dc.subject	Noise Suppression	en
dc.subject	Independent Component	en
dc.subject.other	Airborne hyperspectral data	en
dc.subject.other	Coastal waters	en
dc.subject.other	Data sets	en
dc.subject.other	Dimensionality reduction	en
dc.subject.other	Endmembers	en
dc.subject.other	FastICA	en
dc.subject.other	HyperSpectral	en
dc.subject.other	ICA algorithms	en
dc.subject.other	In-situ measurement	en
dc.subject.other	Independent components	en
dc.subject.other	Independent signals	en
dc.subject.other	Noise suppression	en
dc.subject.other	Statistical problems	en
dc.subject.other	Three-level	en
dc.subject.other	Thresholding	en
dc.subject.other	Water turbidity	en
dc.subject.other	Apartment houses	en
dc.subject.other	Blind source separation	en
dc.subject.other	Discrete wavelet transforms	en
dc.subject.other	Hemodynamics	en
dc.subject.other	Mapping	en
dc.subject.other	Multivariant analysis	en
dc.subject.other	Remote sensing	en
dc.subject.other	Signal processing	en
dc.subject.other	Silicate minerals	en
dc.subject.other	Singular value decomposition	en
dc.subject.other	Speech analysis	en
dc.subject.other	Turbidity	en
dc.subject.other	Water analysis	en
dc.subject.other	Independent component analysis	en
dc.title	Independent component analysis for coastal water mapping using hyperspectral datasets	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/WHISPERS.2009.5289048	en
heal.identifier.secondary	http://dx.doi.org/10.1109/WHISPERS.2009.5289048	en
heal.identifier.secondary	5289048	en
heal.publicationDate	2009	en
heal.abstract	Independent Component Analysis (ICA) is considered to be one of the most recent and successful ways to produce independent components out of the hyperspectral cube. The tool tries to resolve the Blind Source Separation (BSS) statistical problem and has been applied to various case studies of hyperspectral datasets, for dimensionality reduction and separation of independent signal sources, i.e. endmembers. Many ICA algorithms have been proposed in the literature. In this study, the FastICA, JADE, BSS SVD, SONS, NG-OL, and SIMBEC algorithms were applied on airborne hyperspectral data for coastal water mapping. Emphasis was given on water turbidity. In order to enforce the capacities of FastICA, a methodology including the eigen-thresholding Harsanyi-Farrand-Chang noise suppression technique, as well as, three-level Discrete Wavelet Transform (DWT) was developed. Results were compared and evaluated with in situ measurements related to turbidity. ICA algorithms produced quite interesting results. The BSS SVD algorithm was proven the most efficient tool for coastal water mapping. © 2009 IEEE.	en
heal.journalName	WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing	en
dc.identifier.doi	10.1109/WHISPERS.2009.5289048	en