MAWSON THE ASTROBIOLOGIST ROVER: TOWARDS AUTOMATIC RECOGNITIONOF STROMATOLITES

Rodney Li1, Thierry Peynot1, and David Flannery2

1Australian Centre for Field Robotics, The University of Sydney, NSW Australia2Australian Centre for Astrobiology, The University of New South Wales, NSW Australia

ABSTRACT

This work aims at developing a planetary rover capableof acting as an assistant astrobiologist: making a prelimi-nary analysis of the collected visual images that will helpto make better use of the scientists time by pointing outthe most interesting pieces of data. This paper focuses onthe problem of detecting and recognising particular typesof stromatolites. Inspired by the processes actual astrobi-ologists go through in the field when identifying stroma-tolites, the processes we investigate focus on recognisingcharacteristics associated with biogenicity. The extrac-tion of these characteristics is based on the analysis ofgeometrical structure enhanced by passing the images ofstromatolites into an edge-detection filter and its FourierTransform, revealing typical spatial frequency patterns.The proposed analysis is performed on both simulatedimages of stromatolite structures and images of real stro-matolites taken in the field by astrobiologists.

1. INTRODUCTION

Over the last 15 years, several rovers have been sent toMars in search of signs of life. Traditionally, planetaryrovers have had limited autonomy, achieving short dailymissions, mainly reduced to executing a short path fromtheir original location to a pre-defined goal. Scientists onEarth would then observe pictures taken by the rover toestimate the potential interest of rocks in regards to astro-biology. However, the actual performance of the MarsExploration Rovers Spirit and Opportunity is inspiringmore ambitious missions in the future, of both longer du-ration and a higher degree of autonomy.

With an increased length and complexity of missionscomes an increased volume of data (e.g. number of pic-tures taken) collected by the rover, which can make thetask of analysing every single snapshot impractical forscientists. Therefore, future projects involving longer-term missions call for a rover capable of acting as an as-sistant astrobiologist: making a preliminary analysis ofthe collected images that will help to make better use ofthe scientists time by pointing out the most interestingpieces of data.

Figure 1. Planetary rover “Mawson” shown in the MarsYard at the Powerhouse Museum in Sydney, Australia.

This research concerns the development of the Mawsonrovers capabilities as an assistant astrobiologist. Mawsonis a prototype planetary rover developed at the AustralianCentre for Field Robotics. It is exhibited at the Power-house Museum in Sydney, Australia, where it operates ina special area designed to simulate typical Martian terrain(see Fig. 1). It is currently working towards the automaticrecognition of stromatolites from visual images acquiredin the field.

This paper focuses on the problem of detecting and recog-nising particular types of stromatolites. Inspired by theprocesses actual astrobiologists go through in the fieldwhen identifying stromatolites, the processes we investi-gate focus on recognising characteristics associated withbiogenicity, including a columnar or domal morphology,the presence of lamina walls and inter-stromatolite de-trital infill, discontinuous laminae of variable thickness,and the geometries of conical stromatolites. The extrac-tion of these characteristics is based on the analysis ofgeometrical structure enhanced by passing the images ofstromatolites into an edge-detection filter and its FourierTransform, revealing typical spatial frequency patterns.

The proposed analysis will be performed on both simu-lated images of stromatolite structures and images of realstromatolites taken in the field by astrobiologists, primar-ily in Western Australia. As part of this evaluation, therelevance and performance of the features considered forthe recognition process will also be discussed.

The remaining of the paper is the following. Section 2

discusses characteristics of stromatolites that astrobiol-ogists look for in the field, in particular those associ-ated with biogenicity. Section 3 proposes machine visiontechniques to automatic recognise some of these charac-teristics in visual images. Finally, Section 4 provides con-cluding remarks and elements of future work.

2. RECOGNISING STROMATOLITES

Stromatolites as referred to herein are layered sedi-mentary structures produced by sediment binding, trap-ping and/or precipitation as a result of the growth andmetabolism of microorganisms (cf. [1]).

Stromatolites are the most widespread macroscopicbiosignatures for life during the Precambrian, the periodof geologic time covering nine tenths of Earth history,during which life was limited to microscopic organisms.Considering the evolutionary history of life on Earth, itseems likely that extraterrestrial life, if discovered, willalso be microscopic. Accordingly, as astrobiologists be-gin to explore potential past and present microbial habi-tats beyond Earth, the recognition and interpretation ofmicrobial biosignatures, including stromatolites, is set tobecome increasingly important.

Any autonomous stromatolite recognition system mustbe able to distinguish stromatolites from images of su-perficially similar abiogenic sedimentary features. Hu-man palaeontologists can recognise stromatolites in thefield based on the appearance of outcrop, yet difficultiesare still encountered and the biogenicity of some ancientstromatolites is debated eg. [2, 3, 4]. Moreover, humanpalaeontologists on Earth enjoy the considerable advan-tage of laboratory study of collected samples, a capabilityunavailable to autonomous systems deployed in the field.

Despite these difficulties, a system capable of recognis-ing putative stromatolites within a background of otherlaminated sedimentary rocks seems achievable. Ideally, athree dimensional model of a stromatolite is inferred fromthe study of several perpendicular cross-sectional views.This would likely be unachievable in the case of a rover.We have thus focussed on features able to help such a sys-tem distinguish putative stromatolites from a laminatedsedimentary background on the basis of an image of asingle cross-section. Some of these features include theoverall morphology; variable thickness and discontinu-ity of laminae; relationships with associated lamina wallsand interstromatolite detrital infill and; the geometry ofconical stromatolite laminae

Stromatolites occur in myriad morphologies, includingstratiform, domical (Fig. 2) and conical (Fig. 3) shapes.

In the case of stratiform and low-relief stromatolites,identification based on morphological characteristics canbe difficult or impossible. Stromatolites with topographicrelief offer more morphological information on which tobase an analysis, but these shapes may be confused withcommon sedimentary features such as soft sediment de-

Figure 2. Vertical section through a large domical stro-matolite in a planar laminated carbonate background.The image figures a lens cap for scale. Tumbiana For-mation, Fortescue Group, Western Australia.

Figure 3. Cut slab showing a vertical section throughtwo conical stromatolites. Note laminae walls (white ar-rows) and onlapping detrital sediments. Scale bar =2cm. Tumbiana Formation, Fortescue Group, WesternAustralia.

formation, folding (Fig. 4) and ripple cross-stratification(Fig. 5).

Figure 4. Tectonic folding appearing superficially similarto conical stromatolites. Joffre Member, Brockman IronFormation, Hamersley Group, Western Australia.

Figure 5. Laminated columnar stromatolite (below),overlain by climbing ripple cross-stratification (above).Tumbiana Formation, Fortescue Group, Western Aus-tralia.

The discontinuity of stromatolitic laminae is in contrast tothe continuous laminae of many common laminated butabiogenic sedimentary features. In stromatolites, wherelamina growth is initiated and controlled by the presenceof a biofilm or microbial mat, interstromatolite areas of-ten comprise detrital grains of a different size, compo-sition, porosity, colour, and/or lamination (Figs. 3, 5).Stromatolitic laminae may terminate in a sharp boundarycomprising a wall of laminae (Fig. 3), with surroundingsediments onlapping the structure (Figs. 3, 5).

Conical stromatolite geometries are often consideredmore reliable indicators of biogenicity as there are rela-tively few abiogenic sedimentary features for which theymay be mistaken. In particular, the laminae of some coni-cal stromatolites are thicker in the central axis of the stro-matolite than on cone flanks. This thickening of the lam-inae is known as the axial zone and is thought to resultfrom microbial growth and motility [5]. In addition, theminimum steepness of the laminae can be calculated from

vertical cross-section, and where the value is steeper thanthe angle of repose for particulate sediment (see [6]), thebiogenic interpretation is strengthened. Fig. 3 representsan ideal case; a conical stromatolite characterised by lam-inae steeper than the angle of repose for particulate sedi-ment, with distinct lamina walls, and interstromatolite fillcomposed of onlapping detrital grains.

3. AUTOMATIC RECOGNITION USING MA-CHINE VISION

3.1. Edge Extraction

Since the main feature of stromatolites is the laminae,the first type of visual characteristic that an astrobiologistwould consider is lamina structure. Therefore, a naturalfirst step for a machine vision algorithm is to extract thisstructure using an edge detection filter. Using a Sobel fil-ter, the stromatolite image (like the example in Fig. 6(a))can be converted into an edge image. However, despitethe use of a cut block, the quality of the images and thepresence of additional features such as saw scratches re-sult in rather noisy images and the laminae themselves areoften broken into pieces rather than forming a continuousedge. The human eye is able to discern the laminae in theedge images obtained but tuning the thresholds appliedon the Sobel image resulted in either too much noise ortoo few edges detected. In order to improve these results,

(a) Original image (b) Canny image

Figure 6. Example of colour image of columnar stroma-tolite and the corresponding image after application ofthe Canny filter for edge detection.

the Canny filter was used with a relatively high Gaus-sian sigma value of 2 and threshold values of 0.07 and0.15 in MATLAB [11] in order to remove the smaller mi-croedges in favour of the macroedge laminae as shownin Fig. 6(b). This still left laminae split into pieces in-stead of being continuous and was still not completelysuccessful in eliminating all of the noise. The problem isthat although laminae are relatively large edges, withinthe laminae are smaller features that are preferentiallypassed by the filter. This problem is further compoundedby the fact that different stromatolites, even those within

the same image, can vary significantly in their reaction tothe canny filtering.

3.2. Detecting Possible Stromatolite Columns

Columnar stromatolites are characterised by convex lam-inae and vertical accretion from a flat surface. Laminaeshape is typically highly inherited relative to the under-lying laminae, and laminae are often closely spaced to-wards the edge of the column, comprising a “wall”. Thismeans that stromatolitic columns will often appear incross section as an inherited column of stacked horizon-tal edges bounded by vertical walls. We found that the

Figure 7. Broad Stromatolite Image.

horizontal edges can be isolated by passing a thin idealfan filter of width 10 degrees as seen in Fig. 8. This

Figure 8. Horizontal Line Filtered Edge Diagram -Broad.

isolated the horizontal and near horizontal lines as seenin Fig. 9. This image can then be filtered with an av-eraging filter (of large size, 20 pixels square was usedhere) which reveals locations of high horizontal edge den-sity. Using these horizontal edges as a starting point, the

Figure 9. Horizontal Line Density Map.

laminae were traced outwards by taking all points thatwere above a high threshold (80% of the maximum den-sity was used here) and finding all connected points thatwere above a lower threshold (50% of the maximum wasused here). The result was Fig. 10 which gives regions

of possible stromatolite axes which can then be tested forvariable laminae thickness and convexity. Note that de-

Figure 10. Laminae Expansion Diagram.

tected column on the far right of the figure correspondsto the extracted candidate stromatolite sample shown inFig. 6(a).

3.3. Assessing Stromatolite Columns

After possible columnar stromatolites have been found,the edge images were converted to frequency space us-ing an FFT which yielded the (contrast enhanced) powerspectra as seen in Fig. 11 (with application of a loga-rithmic contrast enhancement to help visual illustration).The convex lamination of stromatolites results in a “fan”shape, with the edges of the fan describing the limits ofthe angles composing the convex laminae of the stroma-tolite. This fan shape is clearer on the thresholded FFTspectrum shown in Fig. 11(b). It was obtained by thresh-olding the image with only the points with more than themedian intensity retained.

(a) FFT (b) Thresholded FFT

Figure 11. FFT Spectrum of the edge image of Fig. 6.

In order to verify this result we created a model of thestromatolite structure and obtained the FFT and comparethe results. We used a simple model comprising of a se-ries of parabolas separated by a constant distance (seeFig. 12(a)). This yielded an FFT (Fig. 12(b)) charac-terised by a similar fan shape. Varying the parameters ofour model revealed that the width of the fan is dependentupon the component laminae angles, meaning the extrem-ities of the fan correspond to the extremities of laminaeconvexity. Knowledge of the extremities of the fan thus

allows detection of columnar laminae steeper than the an-gle of repose for particulate sediment, which is a usefulindicator of biogenicity.

(a) Stromatolite Edge Model (b) Stromatolite Model FFT

Figure 12. Computer generated model of the stromatolitestructure and its Fourier Transform.

Isolating the fan shape in the frequency space with a fil-ter and then converting back to image space showed thatdifference was imperceptible and that all the useful in-formation was contained within this fan. There was noperceivable pattern to the phase component of the FFT.

(a) Trimmed FFT of StromatoliteModel

(b) Recovered Structure Model

Figure 13. “Ideal” FFT and recovered structure model.

Therefore, once regions of interest (ROI) containingcolumns that are considered as possible stromatoliteshave been found, the ROI is passed through a Canny filterto extract the structure, then converted to frequency spacewhich will be searched for the distinctive fan shape.

3.4. Angle of Repose

To better identify the limits of the fan shape in the FFTspectrum, the thresholded FFT shown in Fig. 11(b) wassmoothed by averaging (to reduce noise and produce acontinuous distribution with a mask size of 20 pixelssquare). This creates a sharp gradient in the vertical direc-tion for some parts of the fan shape. The result is shownin Fig. 14, where the visible limit of the fan shape gives anevaluation of the maximum angle of steepness in the lam-inae structure. The equivalent angle is also illustrated onthe original colour image of the stromatolite. This max-

(a) FFT (b) Original

Figure 14. Approximate maximum angle of steepness ofthe laminae α = 40◦ as identified on the FFT (a) andshown on the original image (b).

imum steepness was evaluated at about α = 40◦. Sincesand has an angle or repose of about 30◦, it is extremelylikely that this laminae has been formed by biotic pro-cesses. Therefore, there is strong indication that a stro-matolite has been found.

3.5. Convexity of the Laminae

The process we propose will be illustrated with the ex-ample of stromatolite column candidate in Fig. 15(a),that was extracted from a larger rock cross-section(Fig. 15(b)). As mentioned earlier, the limits of the fanshape are necessary to constrain the features of the stro-matolite and can be obtained by thresholding the edge-filtered image and smoothing the resulting image (seeFig. 16).

Fig. 17 shows the profiles of intensity along the blue andgreen vertical lines of Fig. 16(b). The limits of the fanare clearly visible. Taking the derivative of the intensityover these vertical allows to identify the limits of the fanshape that we need.

If corresponding slices are taken an equal distance awayfrom the centre (i.e. frequency 0), then the distance be-tween the graphs is an indication of the orientation of theentire fan shape.

(a) (b)

Figure 15. Potential stromatolite column (a) extractedfrom the full picture in (b) (blue rectangle).

(a) Thresholded FFT (b) Smoothed

Figure 16. FFT of the edge image. Note the fan shape ofthe Fourier Spectrum. The vertical blue and green linesin (b) were added for illustration (see Fig. 15(a)).

Figure 17. Intensity changes in Vertical Cross sec-tions along the blue and green vertical lines shown onFig. 16(b).

.

So far, this process may select concave and convex shapesalike, Since laminae of stromatolites are convex, concaveshapes need to be distinguished and removed. In a convexshape, the centre would be horizontal and the edges on theleft and right would curve downwards (i.e. they wouldhave a specific general direction in which they face), asopposed to concave shapes which curve upwards. Theycan be differentiated by isolating the left part of the lami-nae and finding the direction they face from the FFT andrepeating for the right side.

Note that the maximum steepness for this candidate stro-matolite was evaluated at about α = 60◦ on the FFT (seeFig. 18). Once again, the high steepness of the laminae is

(a) FFT (b) Original

Figure 18. Approximate maximum steepness angle of thelaminae α = 60◦ as identified on the FFT (a) and shownon the original image (b).

an additional strong indication of biogenic content.

4. CONCLUSION

4.1. Discussion

This is a preliminary study in the direction of our goal toendow planetary rovers such as Mawson with some of theskills of human astrobiologists. Our approach is promis-ing for specific types of stromatolites, namely columnarand conical stromatolites with lamina walls.

However, the stromatolite images used in this study weretaken in the most favourable conditions. We also usedcut slabs of known horizontal sections in order to test ourinitial concepts. Real imagery from Mars would be con-siderably more challenging. A natural next step would beto adapt the techniques presented here to a larger libraryof “noisier”, real-world imagery obtained in the field.

4.2. Future Work

In this paper, a method to determine the parameters of the“fan shape” of the FFT has been discussed, however, itrequires a prior determination that the shape found in theFFT image is a fan indeed. This prior step constitutes one

of the main items for future work. An improved methodfor clustering the side laminae also needs to be developed.

Additional avenues of investigation include the differ-ences in colour and grain size between stromatolites anddetrital fill. Remote sensing data could be used to de-termine associated lithological differences and rule outputative stromatolites in non-prospective settings, for ex-ample in volcanic rocks.

ACKNOWLEDGMENTS

This work was supported by the Australian ResearchCouncil, the Australian Space Research Program StreamA, Pathways to Space: Empowering the Internet Gener-ation, the Australian Centre for Field Robotics and theAustralian Centre for Astrobiology.

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