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ABSTRACT
The Neyriz ophiolite occurs along the Zagros suture zone, SW Iran, and is part of a 3000-km obduction belt that was thrust over the edge of the Arabian continent during the Late Cretaceous. This complex lithologically consists of ultramafic unit, layered and massive gabbros, sheeted dykes, and pillow lavas, surrounded by radiolarites and limestone. The TIR and VNIR+SWIR datasets of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument were used for enhancing the rock units. The VNIR-SWIR spectra of field samples were measured and implemented as the potential spectral end-members, as well as the published spectra of the USGS library. ASTER matched-filter and spectral angle mapper (SAM) algorithms as well as minimum noise fraction (MNF) and principal components analysis (PCA) processing were used for lithologic mapping. Output images were compared and evaluated with the field evidences in order to evaluate the capability of each dataset in mapping the lithological units. Results showed that TIR dataset enhances the ophiolite units more efficiently and discriminates the ultramafic, pillow lavas and sheeted dykes as well as the radiolarites and marbles; while the VNIR-SWIR dataset enhances different Cretaceous rock units, northeastern Zagros suture zone.

1. INTRODUCTION
The Neyriz ophiolite, found in a semi-arid environment along the Zargos Thrust Zone, SW Iran, is a well-preserved part of the oceanic lithosphere (Sarkarinejad, 2005). The main ophiolite body outcrops northern the Bakhtegan Lake and predominantly consists of mafic and ultramafic rocks. Adjacent rock units are mainly limestones, exposed at the northeastern and southern parts of the body.

The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) (Fujisada, 1995; Iwasaki et al., 2002) instrument is included on the Earth Observing System (EOS) TERRA platform, and records radiation from the Earth in 14 spectral bands. This instrument, providing enhanced capabilities for geological mapping and mineral exploration, measures reflected radiation in visible-near-infrared region-VNIR, in three bands (between 0.520 and 0.860 µm) and in short-wave infrared region-SWIR, in six bands (from 1.00 to 2.43 µm), with 15-m and 30-m spatial resolution, respectively (Fujisada, 1995). Stereoscopic images can be acquired at 15-m resolution by imaging with the back-looking telescope as well as the nadir-viewing system. Emitted radiation is also measured in five bands in the 8.125- to 11.650-µm wavelength region (thermal-infrared region-TIR) at 90-m resolution. The swath width is 60 km, but the pointing capability to 232 km (Fujisada, 1995).

The purpose of this paper is evaluating and comparing the VNIR-SWIR and TIR data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for mapping the lithological units exposed at the Neyriz ophiolite zone. This paper implements the laboratory spectral features of rock units exposed at the study area as well as spectral curves of the same rock units available from USGS spectral library. In order to determine the effectiveness of ASTER data for mapping the lithologic units outcropped at the Neyriz ophiolite zone several image processing procedures such as spectral angle mapping (SAM) and matched filtering (MF) were explored.

2. GEOLOGY
The Neyriz ophiolite is part of a 3000 km obduction belt that was thrust over the edge of the Arabian continent during the Late Cretaceous. This ophiolite occurs along the Zagros suture zone, southwestern the Sanandaj-Sirjan metamorphic belt, where the Arabian and Eurasian plates have collided, and is considered to be an allochthonous fragment of Tethyan oceanic crust and mantle (Alavi, 1994).

A geological map at scale of 1:100,000 is compiled and published by Geological Survey of Iran (1996). The same geological map as well as field observations was used for comparing the output images to the rock units. The rock units of the study area (53° 52' 00" – 54° 17' 30" E, and 29° 07' 11" – 29° 40' 05" N) occur at five geological zones: 1) Zagros folded zone; 2) Zone of Pichakan radiolarite; 3) Ophiolitic zone; 4) Tertiary flysch zone; 5) Sanandaj-Sirjan zone. The following paragraphs summarize general characteristics of geological Formations and rock units.

A. Zagros folded zone: 1- Jahrum Formation, mainly composed of Biosparite alveolina limestone and marly limestone; 2- Tarbur Formation, which includes the massive limestone with some marly limestone interbeds.

B. Tertiary flysch zone: 1- EOf: Flysch type sediments, sandstone, shale, calcareous sandstone with abundant large olistoliths of orbitolina limestone, and alveolina limestone with minor ophiolitic olistolith; 2- EOof: Flysch type exposures with abundant olistoliths from ophiolites (serpentinite, pillow lava, and radiolarite) in the matrix of EOf.

C. Sanandaj-Sirjan zone: 1- K1: Early Cretaceous orbitolina limestone

D. Ophiolite zone: 1- d.hz: Foliated dunite-harzburgite (serpentinized), minor gabbro-pyroxenite dykes; 2- d: Foliated dunite (serpentinized); l.hz: Alternation of foliated lehrzolite-harzburgite with minor dunite (serpentinized); 3- wb: Alternation of layered webesterite clinopyrexenite, wherlite and dunite; 4- gb: Layered foliated gabbro norite and minor troctolite and clinopyroxenite; 5- K^cm_u ¬: Tectonite mélange of pillow lava, rediolarite, pelagic limestone; 6- K^b_u : Basaltic pillow lavas and minor diabasic dyke; 7- K^d_u : Diabasic sheeted dyke; 8- K^gb_u : Isotropic gabbros; 9- P^sk_z : Massive marble and skarn.

E. Zone of Pichakan radiolarite: 1- JK^r: Turbidite, alternation of radiolarite and red siliceous shale, limestone with minor spilitic pillow lava; 2- R¹: Megalodon limestone turbidite; 3- R^ex_j : Zone of mixed limestone turbidites, radiolarian chert and serpentinite diapir.

3. SPECTRAL FEATURES
Reflectance spectra have been used for many years to obtain compositional information of the Earth surface. Spectral reflectance in visible and near-infrared offers a rapid and inexpensive technique for determining the mineralogy of samples and obtaining information on chemical composition. Minerals commonly display intense absorption features due to electronic processes in transition metals such as Fe, and to molecular vibrational processes in hydroxyl- and carbonate-bearing minerals (Vincent, 1997).

Peridotites and ultramafic rocks as the main components of ophiolites contain more than 40 percent olivine, orthopyroxene, clinopyroxene, and small amounts of hornblende, biotite, garnet and spinel. Reflectance spectra of these minerals are well known (Hunt and Salisbury, 1970; Hunt, et al., 1973; Clark, et al., 1990; Grove et al., 1992). Several studies have been conducted to determine reflectance spectra of ultramafic rocks (for example, Hunt et al., 1974). Olivine reflectance spectra in the 0.4 µm to 2.5 µm wavelength region show a broad asymmetric absorption feature near 1 µm (Burns, 1970) resulting from electronic transitions in Fe2+ Cations. Orthopyroxenes exhibit two main absorption features near 1 µm and 2 µm as a result crystal field transitions in ferrous iron (Burns, 1970) whose positions shift to longer wavelengths with increasing iron content (Adams, 1974; Cloutis et al., 1990). Clinopyroxenes, such as augite and diopside, display two broad absorption bands at 0.77 µm and 1.1 µm as a result of ferrous iron and a rapid fall-off of reflectance toward the blue (Adams, 1974; Hunt and Salisbury, 1970).

Rock chip and hand samples were collected from the Neyriz ophiolite zone and were analyzed spectrally in order to identify the spectral features of the main rock types. Reflectance spectra of the collected samples (Figure 1) were examined in the Geosense mineral exploration company, the Netherlands, using an Analytical Spectral Devices (ASD) spectrometer, which records a reflectance spectrum across an overall spectral range of 350-2500 nm with three separate spectrometers with a 10nm individual band width. VNIR and SWIR reflectance spectra of rock samples were convolved and resampled to calibrated ASTER band positions (Figure 2). Reflectance spectra of some rock samples were selected from the spectral library of united states geological survey (USGS), and were implemented in spectral analyses.


Figure 1: Spectral curves of four main rock types, exposed at the Neyriz ophiolite zone.


Figure 2: Spectral curves of four main rock types, resampled to the VNIR-SWIR bands of ASTER.

The ASTER thermal data could be used in silicate mineral enhancements. The radiation emitted from a surface in the thermal infrared wavelength is a function of both the surface temperature and emissivity. The emissivity relates to the composition of the surface and is often used for surface constituent mapping. A research work with the aim of evaluating ASTER TIR data at Mountain Pass, California (Rowan and Mars, 2003) have showed that quartzose rocks, carbonate rocks, granitic rocks, and intermediate to mafic rocks could be distinguished through analysis of the spectral emissivity data.

4. ASTER DATA ANALYSIS
The ASTER level 1B data acquired on 15 December 2002, is used for the study area. A subset corresponding to the ophiolite zone and its surroundings was derived and Internal Average Relative Reflectance (IARR) calibration was carried out on the data to normalize images to a scene average spectrum. This is particularly effective for reducing hyperspectral data to "relative reflectance" in an area where no ground measurements exist and little is known about the scene. It works best for arid areas with no vegetation. An average spectrum is calculated from the entire scene and is used as the reference spectrum, which is then divided into the spectrum at each pixel of the image. The IARR calibrated dataset was used in principal components analysis approach as well as spectral angle mapping and matched filtering algorithms. The results were compared to the output images from TIR data processing, and validated by the field and map evidences.

4.1. Principal components analysis (PCA)
Principal components analysis is an image enhancement technique for displaying the maximum spectral contrast from n spectral bands. This technique reduces the redundancy contained within the data by creating a new series of images in which the axes of the new coordinate systems point in the direction of decreasing variance. The resulting components are often more interpretable than the original images. The first principal component is a vector that is in the direction of the maximum variance of pixels in the scene. It accounts for more of the spectral variance in the data than any other principal component. The nth component contains all of the remaining variance and separates the most spectrally unique pixels from the rest of the pixels in the scene. All n principal components account for 100% of the variance in the data (Vincent, 1997).

ASTER bands are highly correlated within and between the VNIR (bands 1-3), and SWIR (bands 4-9), which mainly comes from the spectral similarity of most geological materials within and between specific wavelength ranges.

The standard and selective PCA approaches are examined on both VNIR-SWIR and TIR datasets. Performing the standard approach on six SWIR dataset revealed that PC1 is an albedo image; outcrops of marbles were enhanced in PC2 as bright pixels; PC3 showed basic alluvium in bright pixels, while marbles and gabbroic exposures in dark pixels; so this PC image could not be considered as a unique image for enhancing a specific rock unit. PCs 4, 5, and 6 are predominantly noisy images and do not discriminate a rock unit. A color composite of PCs1, 2, and 3 (Figure 3) enhances marbles in yellow, gabbros in cyan, pillow lavas in dark, and radiolarites as well as basic alluvium and peridotites in blue pixels. Selective principal components analysis has already been used on Landsat TM and ASTER data for enhancement of altered areas around specific mineral deposits or districts stained with iron-oxides (Chavez and Kwarteng, 1989; Tangestani and Moore, 2000, 2001; Tangestani et al., 2005). The selective principal components analysis approach applied in this paper consists of calculating the PC images of different subsets of ASTER bands, selected based on the spectral features of rock units in different regions of ASTER instrument. Eigenvector statistics in each PCA would identify the PC image in which the spectral information of mineral is loaded. This information usually represents, in quantitative terms, a very small fraction of the total information content of the original bands, but it is expected that the loaded information relates to the spectral signature of desired mineral. A PC image with moderate to high eigenvector loadings for diagnostic reflective and absorptive bands of a mineral or mineral group with opposite signs, enhances the same mineral (Crosta and Moore, 1989). A selective PCA approach, implemented on bands 1, 4, 6, and 8, enhances marbles in PC2 image in bright pixels. The same approach was performed on bands 10, 12, and 13 in TIR dataset and a color composite image (Figure 4) was generated using the PC images as RGB colors, respectively. Marbles were enhanced in blue pixels at the central parts, north and west of the study area. Radiolarites were enhanced in red, gabbros in green, and pillow lavas in magenta. The basic alluvium with yellow color were separated from peridotites, which were enhanced in green color. The Orbitolina limestones were also enhanced in dark green pixels. Selective PCA using thermal bands discriminates the most various rock types at the study area in comparison to other approaches.


Figure 3: A color composite image produced from PC1, PC2, and PC3 images, output results of PCA analysis on SWIR dataset of ASTER.


Figure 4: A color composite image produced from PC1, PC2, and PC3 images, output results of PCA analysis on TIR dataset of ASTER.

4.2. Spectral angle mapping (SAM)
The Spectral Angle mapping (SAM) is a physically-based spectral classification that uses an n-dimensional angle to match pixels to reference spectra (Kruse et al., 1993). The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. This technique, when used on calibrated reflectance data, is relatively insensitive to illumination and albedo effects. Poorly illuminated pixels would fall closer to the origin than pixels with the same spectral signature but greater illumination. The SAM algorithm generalizes this geometric interpretation to hyper-dimensional space and permits rapid mapping of the spectral similarity of image spectra to reference spectra (Kruse et al., 1993), using the arccosine of the dot products of image and reference spectra as vectors in x-dimensional space. For each reference spectrum chosen in the analysis of an image, the spectral angle is determined for every image spectrum. The outputs of the algorithm are a classification image displaying the best SAM match and a RULE image for each end-member showing the spectral angles in radians between each spectrum in the image and the reference spectrum. Pixels with smaller spectral angles are darker in rule images. Density slice thresholds are used to empirically determine those areas that most closely match the reference spectrum while retaining spatial coherence.


Figure 5: Output image of SAM analysis on the spectrum of pillow lavas. The red areas show the best coincidence with the pillow lavas outcrops.


Figure 6: Output image of SAM analysis on the spectrum of gabbro. The red areas show the best coincidence with the gabbro outcrops.


Figure 7: Output image of SAM analysis on the spectrum of dunite. The red areas show the best coincidence with the peridotite outcrops.


Figure 8: Output image of SAM analysis on the spectrum of marble. The red areas show the best coincidence with the marble outcrops.

The spectra of collected rock samples and the spectra of selected rocks from USGS spectral library were used as the end members for lithological enhancements. The algorithm was implemented on IARR calibrated SWIR dataset and the output RULE images were validated using the geological map and field evidences. The spectra of pillow lavas and sheeted dykes, gabbro and dunite were selected from USGS spectral library, while the spectra of marble was selected from the collected samples. Examination of density slice ranges of Rule image of pillow lavas showed that areas with thresholds 0.003-0.01 and 0.01-0.015 radians coincided the outcrops of pillow lavas at the northern part of main ultramafic mass (Figure 5). The output image for gabbro (Figure 6) shows the relevant areas with thresholds 0.0024-0.01 and 0.01-0.02 radians at the northern and eastern parts of the study area, coincided with the same exposures in geological map. Examination of density slice ranges of Rule image of dunite showed that areas with threshold 0.0047-0.025 and 0.025-0.035 radians were coincided to the main peridotite exposures of the Neyriz ophiolite zone (Figure 7). The output Rule image of marble spectrum shows the ranges of 0.0024-0.01 and 0.01-0.02 radians for the same exposures, mainly in Tang-e-hana and Ghalae-Bahman marble mines (Figure 8).

4.3. Minimum Noise Fraction
Minimum Noise Fraction (MNF) analysis identifies the locations of spectral signature anomalies. This process is of interest to explorationists because spectral anomalies are often indicative of alterations.

The minimum noise fraction (MNF) transform is used to determine the inherent dimensionality of image data to segregate noise in the data and to reduce the computational requirements for subsequent processing (Boardman and Kruse, 1994). This method is similar to principal component (PC) analyses that have been used for a long time in multispectral image processing, but involves an extra preceding step.

The MNF procedure was examined on the TIR dataset of ASTER. The first output image enhances marbles and Orbitolina limestones in bright pixels, peridotites in grey and other rock units in dark pixels. The second component enhances radiolarites in bright, marbles in grey and basic alluvium in dark pixels. The third component also enhances radiolarites in bright pixels, but 4th and 5th components are mostly noisy images and do not enhance any rock unit. Gabbros, pillow lavas and diabasic dykes are not enhanced in any component. RGB color composite of three first components discriminates main rock units in different colors, but the gabbroic rocks are not discriminated from peridotites (Figure 9). This procedure was also implemented on VNIR-SWIR bands. Rock units discriminated in a color composite image generated from first three components were similar to those enhanced in previous colored image.


Figure 9: A color composite image produced from MNF1, MNF2, and MNF3 components, output results of performing MNF algorithm on TIR dataset of ASTER.

4.4. Matched Filtering (MF)
Matched filtering is a technique which performs partial unmixing, finding the abundances of user defined endmembers. Not all of the endmembers in the image need to be known. This technique maximizes the response of the known endmember and suppresses the response of the composite unknown background, thus “matching” the known signatures. It provides a rapid means detecting specific materials based on matches to library or image endmember spectra and does not require knowledge of all endmembers within an image scene. Bands 4-9 have been processed by MF in order to map marbles and Orbitolina limestone, based on the marble spectrum examined from study area samples, and the result was compared to the geological map. The output image enhances all the outcrops of marbles in bright pixels. The same procedure was tested on TIR dataset of ASTER for dunites, gabbros, and pillow lavas using the spectra of USGS. Dunites and gabbros were enhanced in bright pixels, while pillow lavas were discriminated in dark pixels.

SUMMARY AND CONCLUSIONS
The recent development of truly multi-spectral remote sensing systems such as the ASTER instrument potentially offers geologists a cost-effective solution to expensive and time-consuming regional mineral exploration and geological mapping. Subtle spectral reflectance differences recorded in both the VNIR and SWIR wavelength regions are an important basis for identifying specific individual minerals and mineral groups and is a significant advancement over earlier sensors.

Digital image processing techniques such as PCA, spectral angle mapping, and matched filtering were implemented on both VNIR-SWIR and TIR datasets of ASTER using the spectral characteristics of rock units exposed at the Neyriz ophiolite zone. The PCA technique is very effective for discriminating rock units where spectral information is not available and/or ground truth is limited. Performing PCA approach on SWIR dataset enhances marbles, basic alluviums and gabbros. A RGB color composite image generated from PCA outputs of thermal bands 10, 12, and 13 discriminates most various rock types in different colors.

Examining the output Rule images, derived from performing spectral angle mapping on dunite, gabbros, pillow lavas and marble spectra showed that the enhanced areas were coincided on the relevant rock exposures in geological map and as observed in field. Although main rock types are showed in a RGB color composite generated from components 1, 2, and 3, of minimum noise fraction algorithm, gabbros are not discriminated from peridotites in the same colored image. Performing matched filtering on SWIR and TIR datasets using the specra of marbles, dunites, pillow lavas and gabbros results in enhancement of these rock types.

The results obtained from this work on the ASTER data of Neyriz ophiolite zone and adjacent outcrops show that the main lithologies can be mapped using the compositional information derived from spectral characteristics of the samples. It is concluded that although using VNIR-SWIR data enhances some of the ophiolitic units, but spectral similarities between rock units prevents the mapping of all the lithologies, however, these datasets show the carbonate units at the northeastern and southern the study area. It is also suggested that the TIR data of ASTER could successfully discriminate mafic-ultramafic rock units as well as radiolarites, the main lithological units of the ophiolite zone.

ACKNOWLEDGEMENTS
I would like to thank the Research Vice Chancellor of Shiraz University, Iran, for providing the funds and field logistics. I express my gratitude to Dr. Marc Goossens (Director of “Geosense mineral exploration company”, the Netherlands) for his collaboration in accessibility to the ASTER data and examining the spectra of rock samples. I also thank Miss. N. Mazhari for her assistance in data processing.

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