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Characterization of the water optical properties using hyperspectralE. Torrecilla (1,2), J. Piera (1,2), R. Quesada (2), I. Fernández (1), S. Pons (2)

(1) Marine Technology Unit (CMIMA-CSIC).932309500 torrecilla@utm.csic.es.

(2) Dept of Signal Theory and Communications. Technical School of Castelldefels (UPC).

1. IntroductionHyperspectral instrumentation has opened a newdoor for optical oceanography and related fieldsthat make use of optical remote and in situ sensingof the oceans. Hyperspectral information providesoptical oceanographers the potential to accuratelyquantify and classify complex oceanicenvironments, finer-scale features (e.g. bottomtype and characteristics and phytoplanktonblooms), depth-dependent inherent opticalproperties (IOPs) and specific chemicalcompounds [1]. For instance, the hyperspectralinstrumentation has made possible the remoteidentification of different taxonomic groups ofphytoplankton due to that some pigments areunique to individual phytoplankton group orspecies [2].The incorporation of hyperspectral sensors toautonomous sampling platforms of anoceanographic observing system makes essential

pick the seismic phases. Alternatively, WASprocessing is also done using existing tools chieflydesigned for MCS geometries and not all thetechniques are therefore well-suited for properWAS processing. One of the objectives ofSigsensual has been to design a new adaptedsoftware tool allowing to perform all theseoperations using a single, easy to use, modularplatform [2].

In contrast to MCS, wide-aperture WAS acquisitionsystems are specifically designed to record boththe reflections and the continuous refractions ofthe seismic waves propagating through themedium. This makes that the obtained recordsections (Figure 2) do not yield directlyinterpretable images of the sub-seafloor, beingnecessary to built models accounting for thepropagation velocity of the seismic waves in orderto interpret the data.Until very recently, seismic velocity models wereobtained either by forward modelling or by traveltime inversion, and therefore the basic data pre-processing consisted of allow identifying andpicking the most prominent seismic phases

recorded by the system (chiefly first arrivals).However, the fast increasing on computingfacilities have allowed developing tomographytechniques that use not only first arrivals butthe full wavefield. It is therefore necessary todesign more elaborate processing sequencesto make WAS data amenable to this typemethodology.Useful seismic phases present in the recordsections are usually masked by different typesof noise, which may be caused by the instrument(electronic noise, quantification …), by theenvironment (ship noise, cetacean, sea wavecourse, currents …), or even by the signal itself(higher order reverberations and scatteredsignals which obscure later arriving prominentphases). Another of the objectives of Sigsensualproject is to design filters and processingsequences allowing to obtain as much of thevaluable information contained in the recordsections as possible, by improving the signalto noise ratio, as well as removing or attenuatingsome well characterized phases, such as thewater wave or its reverberations [3].

References[1] Sh. Shariat Panahi et al., Characterizationof a High Resolution Acquisition System forMarine Geophysical Applications, Mar. Technol.Workshop, Vilanova i la Geltrú, 2005.[2] I. Rodríguez et al., “A new software tool forWide-Angle reflection/refraction Seismic dataProcessing and Representation (WASPAR),Mar. Technol. Workshop, Vilanova i la Geltrú,2005.[3] S. Ventosa et al., Signal processingtechniques applied to seismic signal detection,Mar. Technol. Workshop, Vilanova i la Geltrú,2005.

Figure 2: Record section (offset-travel timediagram) corresponding to the recording of airgun

shots by an OBS.

to develop new spectral algorithms and techniquesfor the analysis of the hyperspectral data obtained[3] [4].The SAMPLER project [5] coordinates thedevelopment of an oceanographic sonde in orderto measure physical and biological parametersat small scale, and an integrated software packageto analyse all the data obtained at small and largerscales. As it has been described, this integratedinstrumental system will include a newhyperspectral sensor based on LIGAmicrosystems technology (figure 1). This sensorwill be able to measure upwelling and downwellingirradiance spectra within the water column.Apparent and inherent optical properties (AOPs,IOPs) will be estimated from these spectral values.

2. Hyperspectral AnalysisThe development of new spectral techniques for

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Figure 1. LIGA spectrometer and its spectralresponse.

processing the high-resolution data obtained fromthe LIGA spectrometer can aid in thecharacterization of marine ecosystems.

The spectral techniques will be developed in twophases:

First phaseThis will be a theoretical phase and will involveworking with spectral data obtained by simulation.An initial set of spectral signatures with knownfeatures will be used to evaluate how efficient thespectral techniques are. Different in-water lightfields will be simulated using radiative transfernumerical models [6]. The sensor response(spectral sensitivity, spectral resolution and signal-to-noise ratio) wil l be also simulated.

The spectral techniques for the analysis ofhyperspectral data will be:

Derivative analysisDerivative analysis is a powerful tool used in theanalysis of hyperspectral data. It has beendemonstrated how this method enhances smallfluctuations in reflectance spectra and separateclosely related absorption features [7]. Theseabsorption features can be used to extract detailedqualitative and quantitative information about theenvironment evaluated.

Spectra l deconvolut ion and unmixingIn hyperspectral data processing the unmixing ofreflectance or fluorescence data can aid in theclassification process based upon these spectra.In addition, extraction of useful information maybe possible by reflectance deconvolution [8].

Wavelet TransformIt has been demonstrated that the wavelet-basedmethod is practical for derivative analysis ofhyperspectral signatures, specifically for computingscale-space images and spectral fingerprints [9].

Similarity index analysisThe similarity index has been used to correlatemeasured absorption with known phytoplanktonabsorption curves for identification purposes [10].

Second phaseThis phase will be carried out once theoceanographic sonde will be entirely developedand measures from the water column taken with

the hyperspectral sensor will be available. Thespectral techniques developed during the firstphase will be tested with experimental data.

3. AcknowledgementsThe project VARITEC-SAMPLER (CTM2004-04442-C02-2/MAR) is funded from the SpanishMinistry of Education and Science. We thankMaribel Perez and Nuria Pujol for theircollaboration on the design of the hyperspectralsensor control.

4. References [1] Z.P. Lee and K.L. Carder. Effect of spectralband numbers on the retrieval of water columnand bottom properties from ocean color data.Appl. Opt., 41, 2191-2201, 2002.

[2] E. B. Örnólfsdóttir, J.L. Pinckney and P.A.Tester. Quantification of the relative abundanceof the toxic dinoflagellate, Karenia brevis(Dinophyta), using unique photopigments. Journalof Phycology, 39(2) , 449-457, 2003.

[3] Hyperspectral Coupled Ocean DynamicsExperiments (www.opl.ucsb.edu/hycode.html).

[4] A.R. Weeks, I.S. Robinson, J.N. Schwarz andK.T. Trundle. The Southampton underwatermultiparameter optical-fibre spectrometer system(SUMOSS). Meas. Sci. Technol. 10 1168-1177,1999.

[5] J. Piera, E. Torrecilla, I. Fernández, S. Pons.SAMPLER: An instrumentation Project for studyingthe effect of turbulence in aquatic systems.Instrumentation Viewpoint (this issue).

[6] C. D. Mobley. Light and Water, radiative transferin natural waters. Academic Press, 1994.

[7] E. M. Louchard, R.P. Reid, C.F. Stephens,C.O. Davis, R.A. Leathers, T.V. Downes and R.Maffione. Derivative analysis of absorption featuresin hyperspectral remote sensing data of carbonatesediments. Optics Express 26, Vol. 10, 1573-1584, 2002.

[8] S. Leavesley, W. Ahmed, B. Bayraktar, B.Rajwa, J. Sturgis and J.P. Robinson. Multispectralimaging analysis: spectral deconvolution andapplications in biology. Proceedings of SPIE Vol.5699, 2005.

[9] L.M. Bruce and J. Li. Wavelets forComputationally Efficient Hyperspectral DerivativeAnalysis. IEEE Transactions on geoscience andremote sensing 7, Vol. 39, 1540-1546, 2001.

[10] D.F. Millie, O. Schofield, G.J. Kirkpatrick, G.Johnsen and T.J. Evens. Using absorbance andfluorescence spectra to discriminate microalgae.European Journal of Phycology 37, 313-322,2002.