THE APPLICATION OF SUPERVISED CLASSIFICATION TO LANDSAT DATA IN THE EXPLORATION FOR SURFICIAL URANIUM DEPOSITS - AN EXAMPLE FROM WESTERN

AUSTRALIA

E.U. KRISCHE Uranerzbergbau GmbH

Bonn, Federal Republic of Germany F. QUIEL

Inst Photogrammetry

Technical University Karlsruhe Federal Republic of Germany

ABSTRACT

THE APPLICATION OF SUPERVISED CLASSIFICATION TO LANDSAT DATA IN THE EXPLORATION FOR SURFICIAL URANIUM DEPOSITS - AN EXAMPLE FROM WESTERN AUSTRALIA

The digital evaluation of Landsat data (supervised classification) can give substantial support to the regional selection of targets for valley-fill deposits in arid areas. The approach is described with an example from Western Australia.

1. INTRODUCTION

Landsat products are particularly suited for exploration for valley-fill uranium deposits because of the favourable geographic situation of the deposits, namely an arid climate, sparse vegetation and occurrence at or near the surface in drainage channels. In most instances, conventional imagery is used, as described, for example, by Premoli [1], for exploration for calcrete uranium deposits in Australia. Better results, however, are obtained by analyzing the original digital data.

Merifield et al [2] interpreted ratio images (red-green band) of digital data and could distinguish calcrete, caliche and marine limestone from calcareous soils with similar spectral characteristics. Pitt [3] preferred enhancement techniques of digital data and complementary techniques to delineate palaeodrainages in South Australia.

Beaumont [4] successfully used digital data to locate calcrete in the exploration for road construction material.

Both approaches—conventional Landsat paper images and digital data —have also been applied in exploration for uranium in sandstones [5, 6, 7], uranium in alkaline intrusions [8] and uranium in unconformity-related rocks of Lower Proterozoic age [9].

Thts paper describes the results of a test using both conventional Landsat imagery and an evaluation of digital data (principally supervised classification) for the selection of regional exploration targets for uranium in a surficial calcareous environment. The approach is similar to that of Gary and Longman, (quoted by Williams, [10]), with the difference that no printout of supervised classification is used; instead, colour images can provide an easier orientation for field crews.

2. METHODOLOGY

For the test, a satellite scene was chosen covering the Yeelirrie uranium deposit and other deposits in Western Australia (Id No. 82 107011 21 500, Landsat 1, date: 9 May 1975). The image analyzing system used forthe test consisted of a mini-computer (PRIME 500) with 460 mbyte disc capacity, a colour display system, tape drive, printer and alphanumerical terminals, located at the Institute of Photogrammetry, Technical University, Karlsruhe, West Germany.

The following enhancement methods were applied to the digital data:

— linear stretching, to change the contrast of grey tones of each channel to emphasize particular geological features,

— filtering, smoothing of data to remove unwanted noise,

— ratioing, to emphasize the colorimetric variations and to avoid topographic lighting effects, and

— supervised classification, in which the picture elements of a Landsat scene (pixels) are classified according to training classes (i.e. grey tone intervals) chosen by the geological interpreter to represent the main features of the image and the selected target. A maximum likelihood algorithm is applied to calculate for each pixel the probability that it belongs to the specified classes and to assign it to the class with maximum likelihood. This is

done for all four channels for Landsat 1 to 3 products. Hence, it can search the entire satellite frameforsimilar reflectance after a geological interpretation has determined at least one representative training area for the target (in this case calcrete) and the other classes to describe the image characteristics. The scene classified by this procedure is then displayed on a screen in the same number of (pre-selected) colours as training classes used.

In order to optimize the classes and to avoid confusion due to overlapping ranges of tones of each training area, several attempts might be necessary with a possible relocation of the training areas. From our experience, the training area should be as homogeneous in brightness and colour as possible and the target should have its own distinct shape. The size of the training area should be ideally 1 km2 or more.

A further suggestion for this kind of data evaluation is that where possible, the analyses should be reduced to geologically sensible target areas, in this case the actual drainages, thus decreasing computing costs and time [11]. Also, the unwanted interference from rocks having the same or similar reflectances as calcrete can be avoided from unlikely locations elsewhere (e.g. outside drainage areas).

Our experience shows that the delineation of drainages and the selection of training areas can best be done by a field crew using conventional Landsat paper products. After classification, the results are photographs from the colour screen as 35 mm slides, which can be used directly by the field crew during further follow-up.

3. RESULTS

Linear stretching in channels 4, 5 and 7 produced typical blueish to reddish colours over the Yeelirrie calcrete (Figure 1 ) and, in a less pronounced manner, over other calcretes. However, similar colours were also found in fire-affected areas. Filtering and ratioing emphasized the Yeelirrie channel slightly but did not clearly indicate its exposed calcrete. However, calcrete was emphasized by supervised classification.

The enhanced Yeelirrie channel and the location of training areas chosen in order to characterize the main colours and features of this subscene are shown in Figure 2. After the training classes were refined (Figure 3) to avoid overlapping of class intervals, the supervised classification was performed. The resulting image (Figure 4) clearly delineated the main parts of the exposed Yeelirrie calcrete in a typical colour combination (brown, red, light green) and in a distinct configuration. The same training characteristics were now applied to another subscene of interest within the same satellite frame. The same calcrete colour assignments are outlined over drainages in which other (uraniferous) calcretes are known — Hinkler Well, Centipede and North Lake Way (Figure 5). The results fit surprisingly well with published maps (e.g. [12]) showing area of exposed calcrete. As discussed above, this approach can be refined further by applying it only to known drainages in order to avoid unwanted data noise from areas which are not of interest. For example, northwest of the Hinkler Well-Centipede drainage (Figure 6), the classification has "identified" calcrete in an area having only an extensive cover of white quartz scree (R. Dudlley, Esso Minerals Ltd., personal communication 1983).

4. DISCUSSION

The advantage of the described procedure is evident with the help of defined calcrete training areas (minimum size 1 km2), the entire satellite frame (34 225 km2) can easily be searched for similar signatures. Such a procedure can substantially support the regional selection of targets during exploration for valley-fill deposits in arid areas. This could be of especial interest to countries for which only large-scale maps exist (e.g. Somalia, Sudan).

The 35 mm slides or prints have sufficient precision for field crews to be able to locate themselves. The additional accuracy of fully processed and geographically corrected scenes seems generally unnecessary and these are far more costly. During our exploration, many Landsat scenes from central Australia were investigated, and results were followed up successfully by applying the supervised classification.

The main disadvantage is that it is a method only for second-phase reconnaissance programs, because at least one calcrete must be known out of the entire scene in advance in order to be able to select the training classes for the above procedure. Other handicaps can be the exact recognition and location of calcrete training areas, masking effects of vegetation, soils or pedogenic duricrusts covering calcretes and finally, the spectral range of calcrete.

The related procedure of unsupervized classification, in which the data is classified statistically without geological input, has also been applied and used successfully in conjunction with supervised classification. In some instances the combined procedure was more effective than the method used above [11].

ACKNOWLEDGEMENT

The authors wish to thank W.G. Middleton and E. Becker, field geologists for Uranerz Australia (Pty) Ltd, for their kind cooperation, and Dr. C. Butt, CSIRO, Perth for reviewing this paper.

Figure 1

Enhanced full Landsat scene (channels 4, 5, and 7) covering Yeelirrie channel (arrow) and Lake Way (white, northern portion of image) in Western Australia;

vertical run of image runs approximate-ly NNE. This and following images are only roughly geographically corrected,

size of area 185 x 185 km.

Figure 2

Location of training areas for super-vised classification in Yeelirrie subscene; enhanced image from channels 4, 5, and 7, area covers approximately 37.5 x 35km, small training areas cover exposed Yeelirrie

calcrete.

Figure 3

Display of classes in two dimensional feature space show only few overlap-ping classes of the training areas used in Figure 1 (channels 5 and 7). \

Figure 4

Same subscene as Figure 2, super-vised classification outlines Yeelirrie calcrete /length approximately 22 km in NW-SE) in colour combination

brown, red and light green.

Figure 5

Supervised classification over Lake Way subscene with same colour assignments as in Figure 3; exposed calcretes occur north and west of Lake Way drainages (Lake Way — dark blue, its length on picture approximately 30km NW-SE, size of subscene

approximately 52 x 43 km).

Figure 6

Supervised classification over section of subscene from Figure 5, size approximately 19 x 19 km. H inkier Well drainage running E NE- WSW with Centipede deposit (dot); same colour assignment as in Figure 4, area to the NW of Hinkler Well drainage is covered by white quartz scree, having same reflectance as exposed calcrete

areas.

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THE APPLICATION OF LANDSAT IMAGERY FOR DELINEATING POTENTIAL SURFICIAL