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Regional mapping of planetary surfaces with imaging spectroscopy
Plrrtwt..Spacr Sci.. Vol. 45, No. I I. pp. 1371. 1381, 1997
Pergamon
fp 1997 Elsevier Science Ltd. Ail rights reserved Printed in Great Britain 0032-0633197 $17.00+0.00
PII: SOO32-0633(97)00074-3
Regional mapping of planetary surfaces with imaging spectroscopy Giancarlo Bellucci and Vittorio Formisano CNR. Istituto Received
di Fisica dello Spazio Interplanetario.
23 September
1996: accepted
17 March
CP27 00044 Frascati.
Italy
1997
We present a method to determine spectral diffrrenoes a@ compositional variability of planetary d by means of imaging . The quantity frequently sezxsmgof planetary bodies in the is the reflectance spectrum mineralogic composition of Very often, however, this owing to lack of knowlspectral similarity to be uxrethe refkctance spectrum
These new relaal similarities. The the surface under ed to Earth-based
Introduction The detailed analysis of observational data gathered in different regions of the electromagnetic spectrum gives us the clues to identify the exogenic and endogenic processes which have contributed in shaping the surfaces of the Solar System major and minor bodies. As the solar radiation interacts with a solid surface, the intensity and the spectral distribution of the reflected radiation will be
determined by the composition of the soil and by its physical properties. such as porosity, roughness, grain size distribution (Adams, 1974). From a purely spectroscopic standpoint. the composition of the soil under study will introduce spectral features which are peculiar to the mineral assemblage of the soil. Thus, the physical status and the composition of a given surface can be inferred from its observed spectrum ; in addition, local compositional contrast can be tied to morphological features if imaging data. with sufficient spatial resolution, are available for the region under observation. The advent of the space age has provided the technological support needed to perform close observations of planetary surfaces by means of instruments placed on board of orbiters. A typical category of instruments extensively used in remote sensing includes image spectrometers, instruments capable to provide images of a study region in many narrow spectral bands. Therefore, the availability of the spectrum of each pixel in the scene allows to map the mineralogic composition and the geologic units present on a planetary surface. In the following we describe a method to highlight the compositional variability of planetary surfaces using imaging spectroscopy data. This method has been successfully applied to ground-based imaging spectroscopy data of selected regions of the Moon, for which an extensive spectroscopy study has been done in the past. The work reported in the following has been done in preparation of the analysis of the imaging spectroscopy data that the VIMS (visible and infrared mapping spectrometer, Jurgens et al., 1990) instrument on board the Cassini spacecraft, will gather during its tour around the Saturn system of rings and satellites. The method can also be applied to data provided by other imaging spectrometers, such as ISM (Bibring et al., 1989) flown on board the PhoboR mission to Mars and the future VIRTIS instrument (Coradini et al., 1996) to be flown on the Rosetta mission to comet Wirtanen. Background Spectroscopy is a powerful tool for the recognition of surface composition. It has been used in the past for the
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G. Bellucci and V. Formisano:
study of spatially resolved objects such as the Moon and Mars and also in the case of point sources as asteroids and outer satellites. When the spatial resolution is enough, however. it is possible to discriminate between spectrally and mineralogical different units present on the surface. In the case of the Moon for example, spectroscopy allowed mineralogic differences among maria, highlands and craters to be recognized (McCord et al., 1972; Pieters and McCord, 1976; McCord et ul., 1981). Spectroscopy, however. provides only the spectrum of a relatively small region. while mapping of regional differences requires the acquisition of many spectra, which is not always feasible. mainly because of reduced observation time. Also multiband imaging at few diagnostic wavelengths has been used to outline the boundaries of geologic units whose unique composition is then identified by spot spectroscopy (McCord r’/ ul., 1976).
Rqflectunce
spectroscop?’ and multiband
imuging
The quantity normally measured in telescopic studies of a planetary surface in the 0.3-2.5 pm region is the reflectance spectrum defined as the intensity of reflected sunlight from the surface. When observations are made from the Earth, the atmosphere adds its own absorption features and must be removed. Moreover, the instrumental response must be known in order to obtain the exact spectrum of the body. In principle, the corrections for the Earth’s atmosphere and the instrumental transfer function can be done simultaneously by using a star with the same spectral characteristics of the Sun. This procedure eliminates the common features present in the solar and reflected components, and the only remaining absorption bands are due to the surface itself and are linked to the mineralogic composition of the body. In practice this procedure encounters some difficulties linked to the limited number of usable stars and to the lack of sufficient observing time. On the other hand, remote spacecraft observations require a good calibration of the instrumental response while the solar spectrum has been accurately measured and can be used in the existing digital form. Nevertheless, the spectral reflectance is mandatory when the nature of the constituents the surface has to be known. In some cases, however, it is interesting to identify all the regions of a planetary surface which exhibit the same spectral characteristics, with no regard to the absolute composition, producing the so-called regional mapping. Before the advent of imaging spectroscopy, this was done through multiband imaging, acquiring few images in regions of the spectrum where unique absorption features are found. For example, the lunar soils have been mapped in the UV-VIS range through the 415, 730 and 950 nm bands by using the 4151750, 9501730 and 7501415 nm ratios to discriminate between different pvroxene composition, soil maturity and ilmenite content-of lunar soils (Pieters et al., 1994; McEwen et al.. 1994). These ratios are assigned to the three fundamental colors, 415,!750nm controlling the blue, 750/950nm the green and 730/415nm the red and a composite color image is generate where the color tones depict the soil composition. Before using band ratios. however, the transfer function
Mapping
with imaging spectroscopy
of each filter and the detector response must be determined in order to compare one another.
vs. wavelength the ratios with
The imaging spectroscopp?~ upprou’h imaging spectroscopy provides images of a region of interest in many spectral bands. It is then possible to generate compositional maps of the surface under study using the spectrum of each pixel. For brevity. we will call this ensemble of images a spectra image cube. Moreover we will discuss the case of the Moon, because it is a body for which Earth-based observations provide good spatial resolution, allowing the regional geologic units to be mapped. The same ideas can be applied to high spatial resolution imaging spectroscopy data such as those which the Cussini VfMS imaging- spectrometer will acquire during its mission. Our approach to the recognition of compositional units is the following. We normalize the spectra in the cube to the spectrum of a pixel (or an average of pixels) chosen in the image itself. This normalization removes both the instrumental and the atmospheric transfer functions and allows the direct comparison of the spectra while the increased spectral contrast permits the color differences of the pixels in the image to be recognized. Reflectance spectra relative to a uniform mare surface in central Mare Serenitatis (designated MS-2. McCord, 1968) have been used in the past by lunar scientists to enhance spectral differences of lunar soils. Relative spectra have also been used in imaging spectroscopy in the case of Mars (Erard et al., 1991 ; Bell, 1992). In principle, the normalization can be done by using every pixel or region in the image. In practice the best results are obtained if the normalization spectrum NO.) is commensurable with the ith spectrum S,(L), with i = 1, , N,, and Nr, equal to the number of pixels in the image. If N >> S, the relative spectrum Si(i.)/lv(~) will be very flat with small spectral contrast and an average value < 1. In contrast. if N(i.) < s,(n) the resulting relative spectrum will be much more noisy. especially in the 0.4 and 1.1 pm regions where CCD detectors exhibit lower sensitivity. If an average spectrum is used for the normalization, the region over which the average is made must be spectrally uniform within the instrumental precision. Moreover, the number of averaged spectra must not be too large if subtle spatial variations have to be mapped. The further step in our mapping method is the production of a compositional map, or to be precise, a color image where each color is assigned to pixels having similar spectra and hence same composition. Note that now the problem becomes totally mathematical, in the sense that we must find an algorithm to identify all the relative spectra similar within a fixed threshold and group them in a class.
The classification
procedure
For the classification of the spectra we have used the spectral angle mapper classifier (SAM in the following, Yuhas and Goetz, 1993). The SAM is an automated
G. Bellucci and V. Formisano:
Mapping with imaging spectroscopy
method for comparing image spectra with individual spectra. The algorithm determines the similarity between two spectra by calculating the angle between them, treating them as vectors in a space with dimensions equal to the number of bands. The SAM algorithm is insensitive to the average brightness level of each spectrum, (it uses the direction and not the length of the spectra); the spread inside each class respect to the mean will be due only to spectral differences. The SAM algorithm computes the similarity of the current spectrum S to that of a training site T, by using the following equation (Kruse et al.. 1993)
The training site is hypothesized to have spectral characteristics typical of a spectral unit with wider spatial distribution. The angle u in radians is computed for each pixel in the image and its value is put into the final map. In this new image darker pixels represent smaller spectral angles, and thus spectra that are more similar to the training spectrum. The division of each spectrum in the image cube by a reference spectrum, together with the use of an efficient classification algorithm offers two main advantages that we want here to emphasize. The former is that the normalization removes simultaneously the instrument and atmospheric transfer functions allowing us to neglect the total instrumental response (atmosphere-t instrument). On the other hand, the use of the reflectance spectrum to discriminate between different spectral units needs the reduction of the raw spectra to the final usable form. This implies a knowledge of the instrument calibration factors and the atmospheric behavior involving many mathematical operations during the reduction chain, which lowers the final signal-to-noise ratio. The second advantage of our method over traditional techniques can be envisaged in studying a body with an atmosphere, as in the case of Mars. The planetary atmosphere adds its own features to the light reflected by the surface and must be removed if one is interested in studying the soils. For example. in the l-3 pm range the Mars atmosphere has fairly strong bands at I .4. I .9 and from 2.7 to 2.85 !lrn which mask the bands proper of H,O and OH- and whose presence is an indication of clays on the surface. Removal of Mars’ atmospheric CO? absorptions is important for quantitative remote sensing of these important HZ0 and OH- features. If the temperature. pressure and composition of the atmosphere are constant over a certain region, the normalization of the spectra to a reference spectrum eliminates the common features due to the atmosphere. The resulting relative spectra when clustered in different classes. will provide a map of all the regions whose spectra have similar surface signatures. In the case of Mars, it is important to underline that all the atmosphere present in the image cube studied must be homogeneous ; when dealing with large field of views (as in the case of ISM data) some variations can occur, especially close to volcanoes or at the boundaries of deep plains. In these cases, the interpretation of the data requires a comprehensive model of atmospheric effects.
1373 Results We have applied the concepts described above to two image cubes of the Mare Serenitatis-Tranquillitatis region of the Moon. The cubes contain respectively 119 x 256 and 121 x 287 spectra, and each spectrum has 96 points in the 0.4-l. 1 pm range (Ai, = 7.5 nm). The first of the two image cubes contains the MS-2 standard area, respect to which the spectra of this cube have been normalized. allowing a direct and more easy comparison of our results with those obtained by other workers who used the same standard area. The second image cube does not contain MS-2; although the MS-2 spectrum could be obtained through an intercalibration of the two images (they partially cover a common lunar region). the normalization has been done by using the average spectrum of a small area located in Mare Tranquillitatis in order to test the independence of the classification procedure from the normalization spectrum. The image cubes were obtained by using an imaging spectrometer (Bellucci et al., 1992. Formisano et al., 1992) at the Sierra Nevada (Spain) I .5 m telescope in July 1994. The images were taken by scanning the telescope across the lunar surface : during the motion the instrument took all the spectra of the lunar regions imaged by the slit spectrometer. The details of the instrument and data reduction techniques can be found in Bellucci et al., 1997 and will not be mentioned here. In Fig. 1 a sketch of the study region is shown: the areas where the normalization and training site spectra have been taken are indicated. In Fig. 2 the images in the 0.75 pm band are shown ; they have been corrected for an orthographic view in order to be compared with the map of Fig. 1. The results of application of the method to the two image cubes are shown in Fig. 3. The picture shows the maps of the a values computed respect to the average spectrum (10 x 10 spectra) of a training site inside each image. The maps shown in Fig. 3(a) are computed respect to a training area spectrum in Mare Tranquillitatis. in the highlands and on MS-2. Note that now the term spectrum is used to indicate the ith raw spectrum divided by the raw spectrum of the region chosen as normalization. The normalization area has been chosen on MS-2 for the first image cube and on Mare Tranquillitatis for the second one. The horizontal pattern is due to the normalization process which exaggerates the image striping introduced by the telescope movement during the image acquisition. while the two vertical lines are bad or missing data. The Menelaus crater had some spectral points saturated and for this reason its 2 values must not be considered significant. Dark gray tones show pixels that are similar to the respective training site spectrum. The angle values span from about 0.01 for pixels close to each reference region to 0.3 for the most dissimilar spectra. such as the bright area on the Dawes crater. Note that this area shows high angle values in all the maps. indicating that none of the three training site spectra match its spectrum. The results concerning the second image cube are shown on Fig. 3(b). Now the three training areas have been chosen on Mare Tranquillitatis, Montes Haemus and Jansen B crater and each training site spectrum is an average of 10 x 10 pixels. A thorough examination of Fig. 3(b) reveals interesting features. Small fresh craters. present in Mare Trdnquihitatis have been classified as Jansen B type
I374 (see Fig. 3(b)-c) while the larger craters, as Ross, exhibit only a dark ring coinciding with the crater rim. The ring is also present around the Plinius crater whose central part displays highland-like material (see Fig. 3(b)-b). Highland material is also present in a small area east of crater Jansen B. From Fig. 3(b)-a it appears that the inner zones of larger craters as Maclear and Ross are as dark as Mare Tranquillitatis, while the smaller (including Jansen) are very bright, meaning that their spectra are very different from that of the mare. The regional map is obtained by identifying pixels whose 01values are lower than a certain level. These pixels are then colored differently depending upon their respective training area. This is shown in Fig. 4 where all the pixels with o! < 0.020 rad are displayed in color while the black indicates pixels with CI> 0.020 rad. The upper panel of the plate shows the result relative to the first image cube. The green indicates the Mare Serenitatis class, yellow the highlands and red the Mare Tranquillitatis class. The threshold value has been fixed on the basis of the total spectral noise. The three main geologic units appear well separated ; the Dawes crater and part of its ejecta blanket are spectrally strongly different from the training site spectra. This fact can be attributed to immaturity more than to the composition (Bell and Hawke, 1995). On the highlands some mare-like material is present, mainly in correspondence to Lacus Lenitatis. The central part of Plinius crater shows an highland composition while the outer zone is similar to Mare Serenitatis. The study of these findings will be the subject of a future paper. In order to evaluate the mapping process, in the left panel of Fig. 5 the spectra of the training areas are shown, while the mean spectrum of each class is shown in the right part of the figure. The error bars indicate 1 standard deviation from the class mean. The first points in the UV region exhibit an anomalous increase in reflectance due to the poor signal-to-noise ratio. The training spectra match very well within their respective class. The map in the bottom part of Fig. 4 shows the result concerning the second image cube (in color all the pixels with c( d 0.020) having now used the ochre color to indicate the Jansen B class. Note that the Plinius crater appears almost completely yellow colored as the highlands, while the outer ring is black meaning that its spectrum is different from the training spectra. These facts are in agreement with the results found in the first map where the outer part of the Plinius crater was classified as Mare Serenitatis-like material. The classes reported in Fig. 4 do not depend on the albedo. This can be verified by noting that fresh craters and highlands, which are both very bright (see Fig. 2) have been classified as spectrally different. The comparison of the maps shown on Fig. 3 indicates that the classification method does not depend on the normalization spectra, a very important fact because avoids to intercalibrate the image cubes. The classified spectra derived from the second image cube are shown on Fig. 6. They are normalized to a region different from MS-2, and for this reason not comparable with spectra plotted in Fig. 5. Note as the Jansen B spectra exhibit a strong band at 0.9 pm. The normalization to a reference spectrum is of crucial importance for the correct application of the SAM procedure. The reasons are as follows. The SAM algorithm
G. Bellucci and V. Formisano: Mapping with imaging spectroscopy computes the scalar product between two spectra and all the spectral channels are processed. In Fig. 7 a raw average spectrum of MS-2 is shown; it is possible to see how the total transfer function (atmospheric and instrumental) is peaked at 0.7 pm and most of the signal is located between 0.5 and 0.9pm. If we apply the SAM procedure to the raw spectra, the result is driven mostly by the central wavelengths, while the extreme spectral channels will have a lower weight. In the case of the Moon (but also for Mars) in the 0.41.1 pm range a very diagnostic absorption feature is present around 0.95 pm and is due to mafic minerals. This band is scarcely visible in the raw spectrum and mineralogical maps obtained from raw data would be false. Moreover, the UV-VIS part of the spectrum is sensitive to the abundance of ilmenite present in the lunar maria (Pieters, 1978). Therefore, the normalization by a reference spectrum permits all the spectral channels to be weighted in about the same manner, allowing a better discrimination between different spectral units and lithologies. The normalization is not necessary when the aim of the investigator is to map spatially a single absorption band. This is the case, for example, for the band at 4.35 pm recently found in the NIMS (near infrared mapping spectrometer, Aptaker, 1987) spectra of Ganimede (McCord et ~1.. 1996). In this case, the study can be done considering only the spectral channels present at the center and in the wings of the raw band. The method we have presented above requires the choice of training areas with respect to which to classify the remaining pixels in the image. Different methods exist which do not require an a priori knowledge of the taxonomic structure of the data, as for example, G-Mode (Cerroni and Coradini, 1995). This method relies on a statistical procedure that classifies the spectra on the basis of the 22 criterion. The method has been applied by the authors quoted to ISM data in order to identify spatially coherent units on the Mars surface and to individuate albedo and atmospheric effects that affect the classification procedure. Although the SAM method is insensitive to albedo variations, atmospheric effects can play an important role in the correct interpretation of the results. The possibility to choose between different methods is, therefore, very useful. The SAM method is very powerful when applied to solid or icy bodies of the solar system lacking an atmosphere.
Conclusions The method we have presented looks very promising for mapping spectral units present on the surface of a planetary body. The division of each spectrum by a reference one together with the use of a classification algorithm to recognize spectral similarities overcomes problems linked with poor instrumental calibration or a lack of the total instrument transfer function. The procedure is easy to implement on a computer and is simpler and less computationally demanding than other algorithms based on statistical methods. In the case of a planet with an atmosphere the method can provide interesting results if the atmosphere characteristics remain constant over the study
G. Bellucci and V. Formisano:
Mapping
with imaging spectroscopy
Fig. 1. Map of the study region showing major features discussed in the text. Base map is Rand McNally and Company (1980). The terms MS2, HLI and MT1 indicate the location of training sites of image cube 1. MS2 spectrum has been used to normalize all the spectra of the image cube 1. HL2. MT2 and JB indicate the training sites for image cube 2. MT2 has been used to normalize the spectra of image cube 2
Fig. 2.0.75 pm image of the study region. It is a mosaic of two images extracted from the cubes. Each image was corrected for an orthographic view in order to be compared with the map shown on Fig. 1. The spatial resolution is about 3 km per pixel. North is up
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Fig. 3. (a) Maps concerning image cube 1, computed through the SAM algorithm. Dark tones indicate spectra more similar to the training site spectrum. The numbers close to each bar indicate the angles in milliradians. The angle values have been computed by using, respectively, a Mare Tranquillitatis (upper panel), Montes Haemus and Mare Serenitatis (bottom panel) training site. (b) Maps concerning image cube 2, computed through the SAM algorithm. Dark tones indicate spectra more similar to the training site spectrum. The numbers close to each bar indicate the angles in milliradians. The angle values have been computed by using, respectively, a Mare Tranquillitatis (upper panel), Montes Haemus and Jansen B crater (bottom panel) training site
G. Bellucci and V. Formisano:
Mapping with imaging spectroscopy
Fig .4. Map reporting all the pixels with CI< 0.020 rad. Same color indicates the same class. In t Aack the pixels with CI2 0.020 rad are indicated
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Fig. 5. Spectra obtained after the classification applied on image cube 1. Left panel : training site spectra, defined as an average of 10 x 10 pixels taken on image cube 1 in the locations shown on plate 1. The error bar is 1 standard deviation from the mean. Right panel : Average spectra of the classes. The error bar is 1 standard deviation from the mean
G. Bellucci and V. Formisano:
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Fig. 6. Spectra obtained after the classification applied on image cube 2. Left panel: training site spectra, defined as an average of 10 x 10 pixels taken on image cube 2 in the locations shown on plate 1. The error bar is 1 standard deviation from the mean.Right panel : Average spectra of the classes. The error bar is 1 standard deviation from the mean
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G. Bellucci and V. Formisano:
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Fig. 7. Average raw spectrum of MS-2 (10 x 10 pixels corresponding to an area of 30 x 30 km on the lunar surface). Note as the total transfer function (instrumental + atmospheric) is strongly peaked at 0.7 pm. The SAM algorithm provides good results when applied to normalized spectra (see the text)
region. The normalization process, removing the features introduced by the atmosphere, allows the study of the surface alone. The method can be very useful in the recognition of regional spectral differences when the body surface exhibits a low spatial contrast as, for example, Encedalus or the dark side of Japetus. The development of an efficient method to unveil these subtle compositional variations is, therefore, very important. Acknowledgements. We are particularly grateful to the support staff and telescope operators of the Sierra Nevada Observatory. Without their help the observations described in the paper would not been possible. We specially thank Dr. J. L. Moreno and Dr. J. Rodriguez of Institute of Astrophysics of Andalucia for their continuous assistance. The authors wish also to thank Dr. Yves Langevin and two anonymous referees for their detailed reviews that resulted in substantial improvement to earlier version of the manuscript. Funding was provided by ASI and CSIC grants.
Adams, J. B. (I 974) Visible and near-infrared diffuse reflectance spectra as applied to remote sensing of solid objects in the solar system. J. Geo$zy!vs. Res. 79,4829-4836. Aptaker, 1. M. (1987) Near-Infrared Mapping Spectrometer (NIMS) for investigation of Jupiter and its satellites. In Zmuging Spectroscopic II, Vol. 834, ed. G. Vane. SPIE, Bellingham. WA.. Bell, J. F. (I 992) Charge-coupled device imaging spectroscopy of Mars 2. Results and implications for martian ferric mineralogy. Icarus 100,575-597. Bell, J. F. and Hawke, B. R. (1995) Compositional variability of the Serenitatis/Tranquillitatis region of the Moon from telescopic multispectral imaging and spectroscopy. Icarus 118,51-68. Bellucci. G., Formisano, V., Mastracci, F., Adriani, A. and Capaccioni. F. (1992) VNIR, Visible Near Infrared spectrometer for the Mars 94 mission. Proc. SPZE 1780,623-635. Bellucci, G., Formisano, V. and Pinori, S. (1997) Imaging spectroscopy of the Moon : Data reduction-analysis techniques and compositional variability of the Mare Serenitatis-Tranquillitatis region. Planet. Space Sci., accepted for publication.
1381 Bibring, J.-P., Combes, M., Langevin, Y.. Soufflot, A., Cara, C., Drossart, P., Encrenaz, Th., Erard, S.. Forni, O., Gondet, B., Ksanfomality. L., Lellouch, E., Masson, Ph., Moroz, V. I., Rocard, F.. Rosenqvist, J. and Sofin, G. (1989) Results from the ISM experiment. Nature 341, 591-592. Cerroni, P. and Coradini, A. (1995) Multivariate classification of multispectral images: An application to ISM Martian spectra. Astron. Astrophys. Suppl. Ser. 109, 585-591. Coradini, A. and the VIRTIS Science Team (1996) VIRTIS Visible Infrared Thermal Imaging Spectrometer for Rosetta Mission. In Workshop on The Cassini-Huygens Mission: the exploration of the Saturn System (Abstract). Bologna (Italy), November 19-22. Erard, S., Bibring, J.-P., Mustard, J.. Forni, 0.. Head, J. W.. Hutrez, S.. Langevin, Y., Pieters. C. M., Rosenqvist. J. and Sotin, C. (1991) Spatial variation in composition of the Vallis Marineris and Isidis Planitia Regions of Mars derived from ISM data. Proc. 21st Lunar. Sci. Con/I, pp. 437455. Formisano, V., Bellucci, G. and Adriani, A. (1992) The VNIK-m VIMS experiment for Craf/Cassini. Nuol%o Cimento 15C(6), 1179. Jurgens, D. W., Duval, J. E.. Lockhart, R. F.. Langevin, Y., Formisano, V. and Bellucci, G. (1991) Visible and infrared mapping spectrometer for exploration of comets, asteroids and the saturnian system of rings and moons. ht. J. Imu,ging S~~stems Technol. 3, 108-120. Kruse, F. A., Lefkof. A. B.. Boardman, J. W., Heidebrecht, K. B.. Shapiro, A. T., Barloon, J. P. and Goetz, A. F. H. (1993) The spectral image processing system (SIPS)-Interactive visualization and analysis of imaging spectrometer data. Remote Sensing Environ. 44, 145-163 McCord. T. B. (1968) Color differences on the lunar surface. Ph.D. Dissertation, California Institute of Technology, Pasadena, CA. McCord, T. B., Charette, M. P., Johnson. T. V.. Lebofsky. L. A. and Pieters. C. (1972) Lunar spectral types. J. Geophys. Res. 77, 1349-l 359. McCord, T. B., Pieters, C. M. and Feierberg, M. A. (1976) Multispectral mapping of the lunar surface using groundbased telescopes. Icarus 29, l-34. McCord, T. B.. Clark, R. N., Hawke, B. R.. MC Fadden, L. A. and Owensby. P. D. (198 1) Moon : near-infrared spectral reflectance. a first good look. J. Groph,rs. Res. 86, 10883% 10892. McCord, T. B., Granaban, J., Fanale, F.. Hansen, G. B., Hibbits, C. A., Clark, R. N., Carlson. R., Smythe, W. and Hui, J. (1996) Analysis of reflectance spectra for the icy galilean satellites from the Galileo mission Near Infrared Mapping Spectrometer (NIMS) investigations. In Eos Trans. AGU (Abstract), Vol. 77, pp. 46, F445. McEwen, A. S.. Robinson, M. S., Eliason. E. M.. Lucey, P. G., Duxbury. T. C. and Spudis, P. D. (1994) Clementine observations of the Aristarchus region of the Moon. Science 266, 1858-1862. Pieters, C. M. and McCord, T. B. (1976) Characterization of lunar mare basalt types: I. A remote sensing study using reflection spectroscopy of surface soils. In Proc,. 7th Lunur. Sci. Cor$. pp. 2677-2690. Pieters. C. M. (1978) Mare basalt types on the front side of the Moon : A summary of spectral reflectance data. In Proc,. 9th Lunar Planet. Sci. Conf, pp. 2825-2849. Pieters. C. M., Staid, M. I., Fischer. E. M. and Tompkins, G. He. (1994) A sharper view of impact craters from Clementine data. Science 266, 1844-l 848. Yuhas, R. H. and Goetz, A. F. H. (1993) Comparison of Airborne (AVIRIS) and spaceborne (TM) imagery data for discrimination among semi-arid landscape endmembers. In Proc. 9th Thematic Conf. on Geologic Remote Sensing, pp. 503-5 11. Environmental Research Institute of Michigan. Ann Arbor, MI.
Pergamon
fp 1997 Elsevier Science Ltd. Ail rights reserved Printed in Great Britain 0032-0633197 $17.00+0.00
PII: SOO32-0633(97)00074-3
Regional mapping of planetary surfaces with imaging spectroscopy Giancarlo Bellucci and Vittorio Formisano CNR. Istituto Received
di Fisica dello Spazio Interplanetario.
23 September
1996: accepted
17 March
CP27 00044 Frascati.
Italy
1997
We present a method to determine spectral diffrrenoes a@ compositional variability of planetary d by means of imaging . The quantity frequently sezxsmgof planetary bodies in the is the reflectance spectrum mineralogic composition of Very often, however, this owing to lack of knowlspectral similarity to be uxrethe refkctance spectrum
These new relaal similarities. The the surface under ed to Earth-based
Introduction The detailed analysis of observational data gathered in different regions of the electromagnetic spectrum gives us the clues to identify the exogenic and endogenic processes which have contributed in shaping the surfaces of the Solar System major and minor bodies. As the solar radiation interacts with a solid surface, the intensity and the spectral distribution of the reflected radiation will be
determined by the composition of the soil and by its physical properties. such as porosity, roughness, grain size distribution (Adams, 1974). From a purely spectroscopic standpoint. the composition of the soil under study will introduce spectral features which are peculiar to the mineral assemblage of the soil. Thus, the physical status and the composition of a given surface can be inferred from its observed spectrum ; in addition, local compositional contrast can be tied to morphological features if imaging data. with sufficient spatial resolution, are available for the region under observation. The advent of the space age has provided the technological support needed to perform close observations of planetary surfaces by means of instruments placed on board of orbiters. A typical category of instruments extensively used in remote sensing includes image spectrometers, instruments capable to provide images of a study region in many narrow spectral bands. Therefore, the availability of the spectrum of each pixel in the scene allows to map the mineralogic composition and the geologic units present on a planetary surface. In the following we describe a method to highlight the compositional variability of planetary surfaces using imaging spectroscopy data. This method has been successfully applied to ground-based imaging spectroscopy data of selected regions of the Moon, for which an extensive spectroscopy study has been done in the past. The work reported in the following has been done in preparation of the analysis of the imaging spectroscopy data that the VIMS (visible and infrared mapping spectrometer, Jurgens et al., 1990) instrument on board the Cassini spacecraft, will gather during its tour around the Saturn system of rings and satellites. The method can also be applied to data provided by other imaging spectrometers, such as ISM (Bibring et al., 1989) flown on board the PhoboR mission to Mars and the future VIRTIS instrument (Coradini et al., 1996) to be flown on the Rosetta mission to comet Wirtanen. Background Spectroscopy is a powerful tool for the recognition of surface composition. It has been used in the past for the
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study of spatially resolved objects such as the Moon and Mars and also in the case of point sources as asteroids and outer satellites. When the spatial resolution is enough, however. it is possible to discriminate between spectrally and mineralogical different units present on the surface. In the case of the Moon for example, spectroscopy allowed mineralogic differences among maria, highlands and craters to be recognized (McCord et al., 1972; Pieters and McCord, 1976; McCord et ul., 1981). Spectroscopy, however. provides only the spectrum of a relatively small region. while mapping of regional differences requires the acquisition of many spectra, which is not always feasible. mainly because of reduced observation time. Also multiband imaging at few diagnostic wavelengths has been used to outline the boundaries of geologic units whose unique composition is then identified by spot spectroscopy (McCord r’/ ul., 1976).
Rqflectunce
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The quantity normally measured in telescopic studies of a planetary surface in the 0.3-2.5 pm region is the reflectance spectrum defined as the intensity of reflected sunlight from the surface. When observations are made from the Earth, the atmosphere adds its own absorption features and must be removed. Moreover, the instrumental response must be known in order to obtain the exact spectrum of the body. In principle, the corrections for the Earth’s atmosphere and the instrumental transfer function can be done simultaneously by using a star with the same spectral characteristics of the Sun. This procedure eliminates the common features present in the solar and reflected components, and the only remaining absorption bands are due to the surface itself and are linked to the mineralogic composition of the body. In practice this procedure encounters some difficulties linked to the limited number of usable stars and to the lack of sufficient observing time. On the other hand, remote spacecraft observations require a good calibration of the instrumental response while the solar spectrum has been accurately measured and can be used in the existing digital form. Nevertheless, the spectral reflectance is mandatory when the nature of the constituents the surface has to be known. In some cases, however, it is interesting to identify all the regions of a planetary surface which exhibit the same spectral characteristics, with no regard to the absolute composition, producing the so-called regional mapping. Before the advent of imaging spectroscopy, this was done through multiband imaging, acquiring few images in regions of the spectrum where unique absorption features are found. For example, the lunar soils have been mapped in the UV-VIS range through the 415, 730 and 950 nm bands by using the 4151750, 9501730 and 7501415 nm ratios to discriminate between different pvroxene composition, soil maturity and ilmenite content-of lunar soils (Pieters et al., 1994; McEwen et al.. 1994). These ratios are assigned to the three fundamental colors, 415,!750nm controlling the blue, 750/950nm the green and 730/415nm the red and a composite color image is generate where the color tones depict the soil composition. Before using band ratios. however, the transfer function
Mapping
with imaging spectroscopy
of each filter and the detector response must be determined in order to compare one another.
vs. wavelength the ratios with
The imaging spectroscopp?~ upprou’h imaging spectroscopy provides images of a region of interest in many spectral bands. It is then possible to generate compositional maps of the surface under study using the spectrum of each pixel. For brevity. we will call this ensemble of images a spectra image cube. Moreover we will discuss the case of the Moon, because it is a body for which Earth-based observations provide good spatial resolution, allowing the regional geologic units to be mapped. The same ideas can be applied to high spatial resolution imaging spectroscopy data such as those which the Cussini VfMS imaging- spectrometer will acquire during its mission. Our approach to the recognition of compositional units is the following. We normalize the spectra in the cube to the spectrum of a pixel (or an average of pixels) chosen in the image itself. This normalization removes both the instrumental and the atmospheric transfer functions and allows the direct comparison of the spectra while the increased spectral contrast permits the color differences of the pixels in the image to be recognized. Reflectance spectra relative to a uniform mare surface in central Mare Serenitatis (designated MS-2. McCord, 1968) have been used in the past by lunar scientists to enhance spectral differences of lunar soils. Relative spectra have also been used in imaging spectroscopy in the case of Mars (Erard et al., 1991 ; Bell, 1992). In principle, the normalization can be done by using every pixel or region in the image. In practice the best results are obtained if the normalization spectrum NO.) is commensurable with the ith spectrum S,(L), with i = 1, , N,, and Nr, equal to the number of pixels in the image. If N >> S, the relative spectrum Si(i.)/lv(~) will be very flat with small spectral contrast and an average value < 1. In contrast. if N(i.) < s,(n) the resulting relative spectrum will be much more noisy. especially in the 0.4 and 1.1 pm regions where CCD detectors exhibit lower sensitivity. If an average spectrum is used for the normalization, the region over which the average is made must be spectrally uniform within the instrumental precision. Moreover, the number of averaged spectra must not be too large if subtle spatial variations have to be mapped. The further step in our mapping method is the production of a compositional map, or to be precise, a color image where each color is assigned to pixels having similar spectra and hence same composition. Note that now the problem becomes totally mathematical, in the sense that we must find an algorithm to identify all the relative spectra similar within a fixed threshold and group them in a class.
The classification
procedure
For the classification of the spectra we have used the spectral angle mapper classifier (SAM in the following, Yuhas and Goetz, 1993). The SAM is an automated
G. Bellucci and V. Formisano:
Mapping with imaging spectroscopy
method for comparing image spectra with individual spectra. The algorithm determines the similarity between two spectra by calculating the angle between them, treating them as vectors in a space with dimensions equal to the number of bands. The SAM algorithm is insensitive to the average brightness level of each spectrum, (it uses the direction and not the length of the spectra); the spread inside each class respect to the mean will be due only to spectral differences. The SAM algorithm computes the similarity of the current spectrum S to that of a training site T, by using the following equation (Kruse et al.. 1993)
The training site is hypothesized to have spectral characteristics typical of a spectral unit with wider spatial distribution. The angle u in radians is computed for each pixel in the image and its value is put into the final map. In this new image darker pixels represent smaller spectral angles, and thus spectra that are more similar to the training spectrum. The division of each spectrum in the image cube by a reference spectrum, together with the use of an efficient classification algorithm offers two main advantages that we want here to emphasize. The former is that the normalization removes simultaneously the instrument and atmospheric transfer functions allowing us to neglect the total instrumental response (atmosphere-t instrument). On the other hand, the use of the reflectance spectrum to discriminate between different spectral units needs the reduction of the raw spectra to the final usable form. This implies a knowledge of the instrument calibration factors and the atmospheric behavior involving many mathematical operations during the reduction chain, which lowers the final signal-to-noise ratio. The second advantage of our method over traditional techniques can be envisaged in studying a body with an atmosphere, as in the case of Mars. The planetary atmosphere adds its own features to the light reflected by the surface and must be removed if one is interested in studying the soils. For example. in the l-3 pm range the Mars atmosphere has fairly strong bands at I .4. I .9 and from 2.7 to 2.85 !lrn which mask the bands proper of H,O and OH- and whose presence is an indication of clays on the surface. Removal of Mars’ atmospheric CO? absorptions is important for quantitative remote sensing of these important HZ0 and OH- features. If the temperature. pressure and composition of the atmosphere are constant over a certain region, the normalization of the spectra to a reference spectrum eliminates the common features due to the atmosphere. The resulting relative spectra when clustered in different classes. will provide a map of all the regions whose spectra have similar surface signatures. In the case of Mars, it is important to underline that all the atmosphere present in the image cube studied must be homogeneous ; when dealing with large field of views (as in the case of ISM data) some variations can occur, especially close to volcanoes or at the boundaries of deep plains. In these cases, the interpretation of the data requires a comprehensive model of atmospheric effects.
1373 Results We have applied the concepts described above to two image cubes of the Mare Serenitatis-Tranquillitatis region of the Moon. The cubes contain respectively 119 x 256 and 121 x 287 spectra, and each spectrum has 96 points in the 0.4-l. 1 pm range (Ai, = 7.5 nm). The first of the two image cubes contains the MS-2 standard area, respect to which the spectra of this cube have been normalized. allowing a direct and more easy comparison of our results with those obtained by other workers who used the same standard area. The second image cube does not contain MS-2; although the MS-2 spectrum could be obtained through an intercalibration of the two images (they partially cover a common lunar region). the normalization has been done by using the average spectrum of a small area located in Mare Tranquillitatis in order to test the independence of the classification procedure from the normalization spectrum. The image cubes were obtained by using an imaging spectrometer (Bellucci et al., 1992. Formisano et al., 1992) at the Sierra Nevada (Spain) I .5 m telescope in July 1994. The images were taken by scanning the telescope across the lunar surface : during the motion the instrument took all the spectra of the lunar regions imaged by the slit spectrometer. The details of the instrument and data reduction techniques can be found in Bellucci et al., 1997 and will not be mentioned here. In Fig. 1 a sketch of the study region is shown: the areas where the normalization and training site spectra have been taken are indicated. In Fig. 2 the images in the 0.75 pm band are shown ; they have been corrected for an orthographic view in order to be compared with the map of Fig. 1. The results of application of the method to the two image cubes are shown in Fig. 3. The picture shows the maps of the a values computed respect to the average spectrum (10 x 10 spectra) of a training site inside each image. The maps shown in Fig. 3(a) are computed respect to a training area spectrum in Mare Tranquillitatis. in the highlands and on MS-2. Note that now the term spectrum is used to indicate the ith raw spectrum divided by the raw spectrum of the region chosen as normalization. The normalization area has been chosen on MS-2 for the first image cube and on Mare Tranquillitatis for the second one. The horizontal pattern is due to the normalization process which exaggerates the image striping introduced by the telescope movement during the image acquisition. while the two vertical lines are bad or missing data. The Menelaus crater had some spectral points saturated and for this reason its 2 values must not be considered significant. Dark gray tones show pixels that are similar to the respective training site spectrum. The angle values span from about 0.01 for pixels close to each reference region to 0.3 for the most dissimilar spectra. such as the bright area on the Dawes crater. Note that this area shows high angle values in all the maps. indicating that none of the three training site spectra match its spectrum. The results concerning the second image cube are shown on Fig. 3(b). Now the three training areas have been chosen on Mare Tranquillitatis, Montes Haemus and Jansen B crater and each training site spectrum is an average of 10 x 10 pixels. A thorough examination of Fig. 3(b) reveals interesting features. Small fresh craters. present in Mare Trdnquihitatis have been classified as Jansen B type
I374 (see Fig. 3(b)-c) while the larger craters, as Ross, exhibit only a dark ring coinciding with the crater rim. The ring is also present around the Plinius crater whose central part displays highland-like material (see Fig. 3(b)-b). Highland material is also present in a small area east of crater Jansen B. From Fig. 3(b)-a it appears that the inner zones of larger craters as Maclear and Ross are as dark as Mare Tranquillitatis, while the smaller (including Jansen) are very bright, meaning that their spectra are very different from that of the mare. The regional map is obtained by identifying pixels whose 01values are lower than a certain level. These pixels are then colored differently depending upon their respective training area. This is shown in Fig. 4 where all the pixels with o! < 0.020 rad are displayed in color while the black indicates pixels with CI> 0.020 rad. The upper panel of the plate shows the result relative to the first image cube. The green indicates the Mare Serenitatis class, yellow the highlands and red the Mare Tranquillitatis class. The threshold value has been fixed on the basis of the total spectral noise. The three main geologic units appear well separated ; the Dawes crater and part of its ejecta blanket are spectrally strongly different from the training site spectra. This fact can be attributed to immaturity more than to the composition (Bell and Hawke, 1995). On the highlands some mare-like material is present, mainly in correspondence to Lacus Lenitatis. The central part of Plinius crater shows an highland composition while the outer zone is similar to Mare Serenitatis. The study of these findings will be the subject of a future paper. In order to evaluate the mapping process, in the left panel of Fig. 5 the spectra of the training areas are shown, while the mean spectrum of each class is shown in the right part of the figure. The error bars indicate 1 standard deviation from the class mean. The first points in the UV region exhibit an anomalous increase in reflectance due to the poor signal-to-noise ratio. The training spectra match very well within their respective class. The map in the bottom part of Fig. 4 shows the result concerning the second image cube (in color all the pixels with c( d 0.020) having now used the ochre color to indicate the Jansen B class. Note that the Plinius crater appears almost completely yellow colored as the highlands, while the outer ring is black meaning that its spectrum is different from the training spectra. These facts are in agreement with the results found in the first map where the outer part of the Plinius crater was classified as Mare Serenitatis-like material. The classes reported in Fig. 4 do not depend on the albedo. This can be verified by noting that fresh craters and highlands, which are both very bright (see Fig. 2) have been classified as spectrally different. The comparison of the maps shown on Fig. 3 indicates that the classification method does not depend on the normalization spectra, a very important fact because avoids to intercalibrate the image cubes. The classified spectra derived from the second image cube are shown on Fig. 6. They are normalized to a region different from MS-2, and for this reason not comparable with spectra plotted in Fig. 5. Note as the Jansen B spectra exhibit a strong band at 0.9 pm. The normalization to a reference spectrum is of crucial importance for the correct application of the SAM procedure. The reasons are as follows. The SAM algorithm
G. Bellucci and V. Formisano: Mapping with imaging spectroscopy computes the scalar product between two spectra and all the spectral channels are processed. In Fig. 7 a raw average spectrum of MS-2 is shown; it is possible to see how the total transfer function (atmospheric and instrumental) is peaked at 0.7 pm and most of the signal is located between 0.5 and 0.9pm. If we apply the SAM procedure to the raw spectra, the result is driven mostly by the central wavelengths, while the extreme spectral channels will have a lower weight. In the case of the Moon (but also for Mars) in the 0.41.1 pm range a very diagnostic absorption feature is present around 0.95 pm and is due to mafic minerals. This band is scarcely visible in the raw spectrum and mineralogical maps obtained from raw data would be false. Moreover, the UV-VIS part of the spectrum is sensitive to the abundance of ilmenite present in the lunar maria (Pieters, 1978). Therefore, the normalization by a reference spectrum permits all the spectral channels to be weighted in about the same manner, allowing a better discrimination between different spectral units and lithologies. The normalization is not necessary when the aim of the investigator is to map spatially a single absorption band. This is the case, for example, for the band at 4.35 pm recently found in the NIMS (near infrared mapping spectrometer, Aptaker, 1987) spectra of Ganimede (McCord et ~1.. 1996). In this case, the study can be done considering only the spectral channels present at the center and in the wings of the raw band. The method we have presented above requires the choice of training areas with respect to which to classify the remaining pixels in the image. Different methods exist which do not require an a priori knowledge of the taxonomic structure of the data, as for example, G-Mode (Cerroni and Coradini, 1995). This method relies on a statistical procedure that classifies the spectra on the basis of the 22 criterion. The method has been applied by the authors quoted to ISM data in order to identify spatially coherent units on the Mars surface and to individuate albedo and atmospheric effects that affect the classification procedure. Although the SAM method is insensitive to albedo variations, atmospheric effects can play an important role in the correct interpretation of the results. The possibility to choose between different methods is, therefore, very useful. The SAM method is very powerful when applied to solid or icy bodies of the solar system lacking an atmosphere.
Conclusions The method we have presented looks very promising for mapping spectral units present on the surface of a planetary body. The division of each spectrum by a reference one together with the use of a classification algorithm to recognize spectral similarities overcomes problems linked with poor instrumental calibration or a lack of the total instrument transfer function. The procedure is easy to implement on a computer and is simpler and less computationally demanding than other algorithms based on statistical methods. In the case of a planet with an atmosphere the method can provide interesting results if the atmosphere characteristics remain constant over the study
G. Bellucci and V. Formisano:
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Fig. 1. Map of the study region showing major features discussed in the text. Base map is Rand McNally and Company (1980). The terms MS2, HLI and MT1 indicate the location of training sites of image cube 1. MS2 spectrum has been used to normalize all the spectra of the image cube 1. HL2. MT2 and JB indicate the training sites for image cube 2. MT2 has been used to normalize the spectra of image cube 2
Fig. 2.0.75 pm image of the study region. It is a mosaic of two images extracted from the cubes. Each image was corrected for an orthographic view in order to be compared with the map shown on Fig. 1. The spatial resolution is about 3 km per pixel. North is up
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Fig. 3. (a) Maps concerning image cube 1, computed through the SAM algorithm. Dark tones indicate spectra more similar to the training site spectrum. The numbers close to each bar indicate the angles in milliradians. The angle values have been computed by using, respectively, a Mare Tranquillitatis (upper panel), Montes Haemus and Mare Serenitatis (bottom panel) training site. (b) Maps concerning image cube 2, computed through the SAM algorithm. Dark tones indicate spectra more similar to the training site spectrum. The numbers close to each bar indicate the angles in milliradians. The angle values have been computed by using, respectively, a Mare Tranquillitatis (upper panel), Montes Haemus and Jansen B crater (bottom panel) training site
G. Bellucci and V. Formisano:
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Fig .4. Map reporting all the pixels with CI< 0.020 rad. Same color indicates the same class. In t Aack the pixels with CI2 0.020 rad are indicated
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Fig. 5. Spectra obtained after the classification applied on image cube 1. Left panel : training site spectra, defined as an average of 10 x 10 pixels taken on image cube 1 in the locations shown on plate 1. The error bar is 1 standard deviation from the mean. Right panel : Average spectra of the classes. The error bar is 1 standard deviation from the mean
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Fig. 6. Spectra obtained after the classification applied on image cube 2. Left panel: training site spectra, defined as an average of 10 x 10 pixels taken on image cube 2 in the locations shown on plate 1. The error bar is 1 standard deviation from the mean.Right panel : Average spectra of the classes. The error bar is 1 standard deviation from the mean
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Fig. 7. Average raw spectrum of MS-2 (10 x 10 pixels corresponding to an area of 30 x 30 km on the lunar surface). Note as the total transfer function (instrumental + atmospheric) is strongly peaked at 0.7 pm. The SAM algorithm provides good results when applied to normalized spectra (see the text)
region. The normalization process, removing the features introduced by the atmosphere, allows the study of the surface alone. The method can be very useful in the recognition of regional spectral differences when the body surface exhibits a low spatial contrast as, for example, Encedalus or the dark side of Japetus. The development of an efficient method to unveil these subtle compositional variations is, therefore, very important. Acknowledgements. We are particularly grateful to the support staff and telescope operators of the Sierra Nevada Observatory. Without their help the observations described in the paper would not been possible. We specially thank Dr. J. L. Moreno and Dr. J. Rodriguez of Institute of Astrophysics of Andalucia for their continuous assistance. The authors wish also to thank Dr. Yves Langevin and two anonymous referees for their detailed reviews that resulted in substantial improvement to earlier version of the manuscript. Funding was provided by ASI and CSIC grants.
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1381 Bibring, J.-P., Combes, M., Langevin, Y.. Soufflot, A., Cara, C., Drossart, P., Encrenaz, Th., Erard, S.. Forni, O., Gondet, B., Ksanfomality. L., Lellouch, E., Masson, Ph., Moroz, V. I., Rocard, F.. Rosenqvist, J. and Sofin, G. (1989) Results from the ISM experiment. Nature 341, 591-592. Cerroni, P. and Coradini, A. (1995) Multivariate classification of multispectral images: An application to ISM Martian spectra. Astron. Astrophys. Suppl. Ser. 109, 585-591. Coradini, A. and the VIRTIS Science Team (1996) VIRTIS Visible Infrared Thermal Imaging Spectrometer for Rosetta Mission. In Workshop on The Cassini-Huygens Mission: the exploration of the Saturn System (Abstract). Bologna (Italy), November 19-22. Erard, S., Bibring, J.-P., Mustard, J.. Forni, 0.. Head, J. W.. Hutrez, S.. Langevin, Y., Pieters. C. M., Rosenqvist. J. and Sotin, C. (1991) Spatial variation in composition of the Vallis Marineris and Isidis Planitia Regions of Mars derived from ISM data. Proc. 21st Lunar. Sci. Con/I, pp. 437455. Formisano, V., Bellucci, G. and Adriani, A. (1992) The VNIK-m VIMS experiment for Craf/Cassini. Nuol%o Cimento 15C(6), 1179. Jurgens, D. W., Duval, J. E.. Lockhart, R. F.. Langevin, Y., Formisano, V. and Bellucci, G. (1991) Visible and infrared mapping spectrometer for exploration of comets, asteroids and the saturnian system of rings and moons. ht. J. Imu,ging S~~stems Technol. 3, 108-120. Kruse, F. A., Lefkof. A. B.. Boardman, J. W., Heidebrecht, K. B.. Shapiro, A. T., Barloon, J. P. and Goetz, A. F. H. (1993) The spectral image processing system (SIPS)-Interactive visualization and analysis of imaging spectrometer data. Remote Sensing Environ. 44, 145-163 McCord. T. B. (1968) Color differences on the lunar surface. Ph.D. Dissertation, California Institute of Technology, Pasadena, CA. McCord, T. B., Charette, M. P., Johnson. T. V.. Lebofsky. L. A. and Pieters. C. (1972) Lunar spectral types. J. Geophys. Res. 77, 1349-l 359. McCord, T. B., Pieters, C. M. and Feierberg, M. A. (1976) Multispectral mapping of the lunar surface using groundbased telescopes. Icarus 29, l-34. McCord, T. B.. Clark, R. N., Hawke, B. R.. MC Fadden, L. A. and Owensby. P. D. (198 1) Moon : near-infrared spectral reflectance. a first good look. J. Groph,rs. Res. 86, 10883% 10892. McCord, T. B., Granaban, J., Fanale, F.. Hansen, G. B., Hibbits, C. A., Clark, R. N., Carlson. R., Smythe, W. and Hui, J. (1996) Analysis of reflectance spectra for the icy galilean satellites from the Galileo mission Near Infrared Mapping Spectrometer (NIMS) investigations. In Eos Trans. AGU (Abstract), Vol. 77, pp. 46, F445. McEwen, A. S.. Robinson, M. S., Eliason. E. M.. Lucey, P. G., Duxbury. T. C. and Spudis, P. D. (1994) Clementine observations of the Aristarchus region of the Moon. Science 266, 1858-1862. Pieters, C. M. and McCord, T. B. (1976) Characterization of lunar mare basalt types: I. A remote sensing study using reflection spectroscopy of surface soils. In Proc,. 7th Lunur. Sci. Cor$. pp. 2677-2690. Pieters. C. M. (1978) Mare basalt types on the front side of the Moon : A summary of spectral reflectance data. In Proc,. 9th Lunar Planet. Sci. Conf, pp. 2825-2849. Pieters. C. M., Staid, M. I., Fischer. E. M. and Tompkins, G. He. (1994) A sharper view of impact craters from Clementine data. Science 266, 1844-l 848. Yuhas, R. H. and Goetz, A. F. H. (1993) Comparison of Airborne (AVIRIS) and spaceborne (TM) imagery data for discrimination among semi-arid landscape endmembers. In Proc. 9th Thematic Conf. on Geologic Remote Sensing, pp. 503-5 11. Environmental Research Institute of Michigan. Ann Arbor, MI.