Proceedings 5th EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, April 23-25 2007
1
GEOLOGIC MAPPING ON MARS BY SEGMENTATION OF OMEGA DATA Harald van der Werff1, Frank van Ruitenbeek1, Tanja Zegers2 and Freek van der Meer1,2 1. ITC, Department of Earth Systems Analysis, Enschede, Netherlands;
[email protected] 2. Utrecht University, Faculty of Geosciences, Utrecht, Netherlands ABSTRACT The OMEGA instrument onboard of ESA's Mars Express mission is the first hyperspectral sensor that has collected data from Mars. The OMEGA team has shown that Mars has considerable surface compositional variation. Spectral interpretation and mineral mapping, however, is difficult on a pixel-by-pixel basis due to sensor noise, an atmosphere dominated by carbon dioxide and especially an unknown surface cover. An object-based segmentation approach is for datasets that are acquired in areas from which we do not have a-priori knowledge useful to ignore the scene-wide effects of the unknown atmosphere and to enhance the spectral contrast of the planet's surface, without any human bias. Unlike common segmentation procedures where distances in feature space are used for pixel similarity criteria, the OMEGA data is segmented using similarity criteria based on spectral absorption feature parameters such as position, depth and area. This paper shows the first results of an object-based processing of OMEGA data and discusses possibiliteis of future development. INTRODUCTION OMEGA (Observatoire pour la Mineralogie, l'Eau, la Glace et l'Activite) onboard of ESA's Mars Express mission is the first hyperspectral instrument that has collected data from Mars. The spectral classification results from the OMEGA team show that Mars has considerable surface compositional variation (i). The first data was released to the scientific community in April 2005. OMEGA collects data with a 1.2 mrad instantaneous field of view. Due to the elliptical orbit of Mars Express, the spatial resolution varies from 300 m. to 4 km. Spectra are collected in 352 contiguous channels in the range of 350 - 5100 nm. The VNIR detector uses a pushbroom technique to observe from 0.36 to 1.07 μm with a 7 nm sampling interval. The SWIR is covered by 2 detectors that use a whiskbroom technique. The first SWIR detector covers 0.93 to 2.7 μm with a 14 nm sampling interval, the second detector covers 2.6 to 5.2 μm with a 20 nm sampling interval. The datacube also contains geometric information, namely pixel longitude, latitude and also altitude, which is based on MOLA (Laser altimeter) data. OMEGA data is corrected for bad channels, solar illumination and instrumental channel to channel gain, using software provided by the OMEGA science team. A first order atmospheric correction is based on spectral differences between the base and the top of the 27 km high Olympus Mons, under the assumption that surface composition is homogeneous. This only corrects the IR channels, only corrects for a small part of the atmospheric column and does not correct for dust or other particles in the atmosphere. The atmosphere of Mars consists mainly of carbondioxide. Although the atmosphere has a pressure of only ~6.5 mbar on the surface, it can contain large amounts of dust that may completely obscure the surface during a global dust storm. Dust is also present as a fine layer that covers extensive parts of the surface. Together with the strong atmospheric influence and other spectral noise, the interpretation of OMEGA spectra is not straight forward, especially not on a pixel-by-pixel basis. With the increase of computing power in the recent years, object-based image processing has become increasingly popular in remote sensing. Object-based approaches intend to assess both the spectral and spatial (shape, texture) information that is present in remotely sensed images. This form of image interpretation resembles human vision and interpretation much closer than a traditional pixel-based method could offer.
METHODS In the analysis of hyperspectral data for geologic or mineralogic mapping, most researchers either use spectral matching techniques aimed at statistical comparison of known target and unknown image pixels or subpixel (spectral unmixing) techniques aimed at understanding the spectral mixing in a pixel (ii). A pixel-based processing can give reliable results when the result is tuned towards prior knowledge of the remotely sensed surface. When this prior information is not present or only limited, as is in the case of planetary remote sensing, the quality of processing results is likely to be low. There are various approaches to incorporate spatial information into classification which can potentially be exploited to add the spatial component to subpixel analysis of remote sensing data (iii). In this research, we chose to use a region-growing segmentation technique to cluster image pixels into objects based on spectral properties (iv). The segmentation is done with an IDL algorithm that calculates absorption feature parameters for every pixel and compares the obtained parameters in the segmentation process. The calcualtion of these parameters is done as described in the manual of the DISPEC program (v). A first advantage is that spectral absorption features are enhanced, which is needed as the data is dominated by albedo differences (Figure 1). A second advantage is that spectral differences can directly be interpreted in terms of mineralogy as the main absorption features for pixels and the obtained objects are known. A third advantage is that the segmentation threshold (similarity criterion) can be set according to our knowledge of mineral spectra from Earth, rather than guessing a statistical fit such as spectral angle or euclidean distance. Segmentation is started at pixels which have a relative low local variance. This local variance is calculated as the mean variance between spectra in a non-overlapping “3x3 pixel” kernel. During the segmentation process, a region can grow as long as neighbouring pixels are found to be similar according to the criteria set. In this experiment, the similarity criterium is the wavelength position of the 10 deepest absorption features in each pixel's spectrum, which was set to 15 nm shift at maximum for each feature. When a region cannot grow any further, the next seed with low local variance is chosen. This process is continued until the whole image has been segmented.
Figure 1: On the left a color composite of a NIR and two SWIR bands of the OMEGA datacube. This image is dominated by albedo differences. On the right a color composite of the same bands with a normalized albedo.
Proceedings 5th EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, April 23-25 2007
3
RESULTS As a first step in the segmentation process, the local variance was calculated (Figure 2). This result shows that this approach works to find relatively homogeneous areas. The result is, however, severely influenced by noise of the detector array and also suffers from an unknown diagonal artefact in the top half of this particular image. The present result of the segmentation consists of an image with object numbers in sequence of formation (Figure 2).
Figure 2: On the left the local variance calculated in a non-overlapping “3x3 pixel” kernel. This result does not only show differences in surface homogeneity, but also several artefacts due to sensor noise. On the right the object ID's obtained in the segmentation process. DISCUSSION & CONCLUSIONS Using spectral absorption feature parameters as a similarity criterion is sensitive to spectral variation in the OMEGA data, as several objects in figure 2 can be recognized as tone differences in the color composite images in figure 1. Also is clear that several large objects have formed at areas where the local spectral variance is low and that multiple, often irregularly shaped and small, objects were formed at pixels where a relative high local variance had been found. Although the use of absorption feature parameters in the segmentation process give positive results, their use should not be limited to the region growing process. Common similarity criteria such as euclidean distance or spectral angle would not reveal much information on which spectral differences have been found. The direct comparison of a limited number of absorption features, as done in our research, is likely to give direct response to a researcher on which specific spectral differences occur and to which compositional (mineralogical) differences they relate. In order to come to such a spectral and lithological understanding, the segmentation output needs to be linked to a table showing the absorption features or even spectral signature of each object. Future plans are consequently to extend this segmentation procedure with a tool to visualize the spectral differences between different objects to enhance expert interpretation. Also is planned to enhance the segmentation process itself by using textural information (e.g.,vi) obtained from HRSC stereo camera images, and to involve shape parameters (e.g.,vii) to get a more compact grow of objects.
REFERENCES
i
Bibring JP, Y Langevin, J Mustard, et al., 2006. Global mineralogical and acqeous Mars history derived from OMEGA/Mars Express data. Science, vol. 312, 400-404.
ii
Van der Meer F & S de Jong, 2000. Imaging Spectrometry: Basic Principles and Prospective Applications. Kluwer Academic Publishers, Dordrecht, the Netherlands, 451 pp.
iii
De Jong, S. & F van der Meer, 2004. Remote sensing image analysis: including the spatial domain. Kluwer Academic Publishers, Dordrecht, the Netherlands, 375 pp.
iv
Haralick, R & L Shapiro, 1985. Image segmentation techniques. Computer Vision, Graphics and Image Processing Letters 1 (4), 132-133.
v
Grove C, S Hook & E Paylor II, 1992. Laboratory reflectance spectra of 160 minerals 0.4 – 2.5 micrometers. JPL publication 92-2.
vi
Lucieer A, A Stein & P Fisher, 2005. Texture-based segmentation of high-resolution remotely sensed imagery for identification of fuzzy objects. International Journal of Remote Sensing, 26 (14): 2917-2936.
vii
Glasbey, C & G Horgan, 1995. Image analysis for the biological sciences. John Wiley & Sons Ltd., CHichester.
