Lab8_ImageRatios

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Geog/Geol 455/ 655

Lab 8: Mineral Exploration with Image Ratios

Objectives1. Understand the principal of image ratios for enhancing subtle absorption features

and suppressing topographically influenced illumination differences.2. Learn how to design a suitable ratio from a mineral spectrum.3. Understand the limitations of a ratio.4. Learn to use one of the most powerful tools in the program Imagine, the Spatial

Modeler.

IntroductionMineral exploration using satellite imagery has been very successful in arid areas,particularly in Nevada. In this lab, you will use various band combinations and ratios in anattempt to find a mineral deposit, and make us all millionaires!

Hydrothermal alteration

In this lab we are searching for economic ore minerals. An economic deposit might in facthave a very low percentage of the mineral we are looking for. For example, gold may beeconomically mined at great depth at 5 ppm (5 grams in a ton of rock!) Clearly, we cannot

always look directly for the economic mineral spectrum in our image. Furthermore, oftenthe economic mineral may not have a particularly separable spectrum in the wavelengthswe are using.

A typical geological exploration approach is therefore to build a model of associatedminerals or environments where the economic mineral might be. In this lab, we shall usethe association of hydrothermal alteration with economic minerals in order to find targetexploration areas. Hydrothermal alteration occurs when hot fluids filter through host rocks.

As the fluids cool, they precipitate minerals that may be of economic significance. Theyalso tend to change the host rock lithology, hence the term "hydrothermal alteration", whichsimply means changing rock mineralogy by hot water. Thus in this lab we would like toidentify the types of altered rocks which have been changed by the hot waters that often

also carry economic minerals.

A. Ratio technique using Landsat data

Digital data has the rather special advantage over photographic information in that you canenhance the particular spectral attributes of interest to you. The early work using MSSdata with four bands in Nevada showed that ratios could be used to enhance mineralalteration (Rowan et al, 1974.) In this lab you will attempt to enhance mineral alterationzones at Cuprite, Nevada, using simulated six band Thematic Mapper imagery. The ratiotechnique is explained below.

A ratio is created by dividing brightness values, pixel by pixel, of one band by another. The

primary purpose of such ratios is to enhance the contrast between materials by dividingbrightness values at peaks and troughs in a spectral reflectance curve. This tends toenhance spectral differences and suppress illumination (topographical) differences. Ratioscan be used to differentiate materials if those materials have characteristic spectra. Ratiosalso subdue topographic shadowing effects. A false color composite can then be made inwhich each ratio band is then assigned one of the three primary colors with the lighterparts (high DN values) of the band contributing more color to the composite.


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Figure 1. Hypothetical spectral reflectance curves, converted from reflectance to DNValues recorded by the sensor, for two cover types, A and B. Three image

bands, each with a spectral width of 0.1 m (0.6, 0.8 and 1.0 m), are shown.

Figure 1 is a hypothetical example comparing field-derived spectral information to the datacollected by a 3-band sensor. Now, lets see what ratios from these three bands wouldgive us the best separation of cover type A from B. Specifically we want an image thathighlights cover type A in bright tones, andBin dark tones. Fill in the table below,estimating the value that the sensor would record.

Band Integrated D.N Values for Band

Wavelength (m) Material A Material B0.6

0.8

1.0

Now try developing your own ratio. I have done one as an example, but it wasnt verysuccessful, because the two cover types are not separable. See if you can get a betterresult with a more carefully chosen set of bands based on the absorption features in Figure1.

Band Ratio A B

(BandX /BandY)

DN ValuesDNX / DNY

ResultingRatio Value

DN ValuesDNX / DNY

ResultingRatio Value

1.0 / 0.6 190 / 190 1.0 220 / 200 1.1

0

50

100

150

200

250

0.4 0.6 0.8 1 1.2

Wavelength (m)

A

B

DNValue

1.00.80.6


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B. IMAGE PROCESSING

Do not pro ceed before you have completed the theoretical exercise above. This

exercise wil l not make sense otherwise.

The data for this exercise comprises a TM image with six bands (there is no thermal, or

band 6). This data is simulated Thematic Mapper imagery of Cuprite Nevada and has 20meter by 20 meter resolution. (It was simulated from 224 band AVIRIS data) Thefollowing table shows the wavelength and spectral region of the TM scene:

Table 1. TM Wavelength Bands.

Band TM Band Region Wavelength

1 1 Blue-green 0.45 - 52 m

2 2 Green 0.52 - 0.60 m

3 3 Red 0.63 - 0.69 m

4 4 Near IR 0.76 - 0.90 m5 5 Mid IR 1.55 - 1.75 m

6 7 Mid IR 2.08 - 2.35 m

Spectral curves from the lithologies present in the scene, and a geological map of the area,are presented on the next page. Note that although in this single band representation thealtered region stands out as a bright area, it is hard to identify the different altered regions(e.g. to discriminate the opalized from the silicified) because differences in aspect result ina large variation in overall illumination. The aim of this lab is to improve on thisdiscrimination and to map the area to the West of the highway using band ratios.


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Geological Map

Alteration Map

Figure 2: GeologicalMaps from: Figure 1, Rastet al., 1991, An evaluationof techniques for theextraction of mineralabsorption features fromhigh resolution remotesensing data.PhotogrammetricEngineering and RemoteSensing57: 1303-1309.


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Figure 3. Cuprite 1.55-1.75 m image

The ratios you choose should be selected based on the spectral reflectance curves

in Figure 4 and the positions of the TM bands. Only calculate ratios that you thinkmay be useful.

To do this, carefully draw the edges of each TM band on the spectral reflectancegraphs (Figure 4). then fill in the table with values corresponding to each geologicfeatures spectra. Now, use the principals discussed in part B to identify the ratiosthat enhance alteration cover-types you select with distinct absorption features.Remember that your ratio needs to enhance the cover-type of interest - thereforeyou need a big number for the ratio of that cover-type, but a comparatively smallnumber for other cover-types. Your ratios do not have to be with adjacent bands,and you might use at least one of the same bands for each ratio.

In doing this exercise it is important to keep in mind that for each ratio you aretrying to enhance ONE of the alteration types relative to all the rest. Thus youshould select a band ratio that gives you the largest ratio for the cover type ofchoice compared to all the rest. If a rock type of interest has no significantabsorption features there is no way a ratio will help enhance the rock type.Therefore you may not be able to design a ratio for each of the three alterationtypes.

Unaltered

rocks (not an

exploration

target)

Bright tones are

the altered rocks

Central Silicified

Core

Opalized Region

Marginal

ArgillizedRegion

US 95


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Note that in Figure 4 the altered rocks (the target of this exercise) are shown in thelower three curves. We are not interested in enhancing the country(background) rock of the Spearhead Member.

Figure 4.Field spectra (After Figure 2 of Abrams, et al., 1977, Mapping of hydrothermalalteration in the Cuprite mining district, Nevada, using aircraft scanner images for the

spectral region 0.46 to 2.36 m. Geology5: 713-718.)

Sim.

Band #

Integrated DN Values for each Band

Devitrified (A) Silicified (B) Opalite (C) Argillized (D)1

2

3

4

5

6

A

B

C

D


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4. Specify Input Image details: Double click on the top image icon. In the window thatopens, click the open button and select the input image (cuprite.img). You needto specify that the digital numbers are float (so that we can have decimal places in ourratios). In the drop-down box under Declare as:select Float. Click ok.

5. Specify Output Image details: Double click the bottom image icon. In the window thatopens, click the open button and NAME the output image ( type in

cuprite_ratio1.img). Change the 8 bit unsigned to Float Single (click on 8 bitunsignedits a dropdown menu).

6. Specify the process details: Double click on the process icon. In the FunctionDefinition window (Figure 7) you will see a list of available bands (on the left), an emptywindow to write the formula (at the bottom), a keypad for numbers (middle), and amenu listing potential functions (on the right). For this part of the exercise we onlyneed a ratio, so we can construct the formula by clicking the input bands and thekeypad (use / for divided by).

Figure 7. Function definition dialogue box, with a band (5-6)/(5+6) ratio specified.

7. Save your model (File > Save > Open Button > Navigate to your folder).8. In the Model window, click on the red lightning bolt to run the model.9. Back in the main Erdas window, open a view (File > New > 2D View), and open the

image you just created (cuprite_ratio1.img) to view the results. Note that ratios arevery noisy, so dont be too disappointed if the results dont look that impressive.

10. To create a new model you can save the model as a new name, and then modify theprocess of the existing model with a different ratio. Dont forget to change the outputfile name (cuprite_ratio2).

Construct a Non-Standard False Color Composite (from ratios)It would be useful to look at the three ratios simultaneously, in order to see the patternsmore clearly. One way of doing this is to make a false color composite - for example byputting the one ratio on the blue channel, the other on the red, and using the green foreither a third ratio or else an original TM band.1. Once you have the three ratios, you will construct a non-standard false color composite

of them. Although Imagine has a tool to do this, we will use the modeler to do so.2. Therefore, create a new model with three input and one output images (Figure 8).

$n1_cuprite(1) / $n1_cuprite(4)


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Figure 8. A model with three input images and one output image

3. Assign the input images to the three ratio files youve just created. Set the output file

name and make sure the output is set to float single.

Figure 9. Function definition for the stacklayers command.4. Enter the formula: From the Functions dropdown, select Data Generation, and click on

the Stacklayer command (Figure 9). Note that Imagine automatically assigns the filenames to the arguments if you click on arg#, and then click on the appropriate inputfrom Available Inputs (left column). Be sure to remove the last comma and trailing dots(these are to indicate you can add more bands).

Create Your Report:

1. Open a new 2D View in the main Erdas window. Open the composite you just created.2. Close other viewers then send the image to Word: Manage Data tab > Send to Word3. Answer the following questions:

1) Describe the ratio you designed in the first (theoretical) part of the exercise.Explain your choice with reference to the spectral reflectance of the two covertypes shown in Figure 1.

2) Describe and explain the ratios you chose for the Cuprite image. Explain whatmineral each ratio was designed to enhance, how you expected that cover typeto look on the image (bright or dark), and whether you feel it was a success.


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REFERENCES

Abrams, M. J., Ashley, R. P., Rowan, L. C., Goetz, A. F. H., and Kahle, A. B. (1977). Mapping ofhydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images forthe spectral region 0.46 to 2.36 mm: Geology, 5: pp 713-718.

Abrams, M. J., Conel, J. E., and Lang, H. R. (1984). The Joint NASA/Geosat test case project. TheAmerican Association of Petroleum Geologists, Tulsa, Oklahoma.

Ashley, R. P., and Abrams, M. J. (1980). Alteration mapping using multispectral images. Cupritemining district, Esmeralda County, Nevada. USGS Open File Report 80-367.

Chavez, P. S., and Kwarteng, A. Y. (1989). Extracting spectral contrast in Landsat ThematicMapper image using selective Principal Components Analyses. Photogrammetric Engineering andRemote Sensing, 55(3): pp. 339-348.

Crippen, R. E. (1987). The regression intersection method of adjusting image data for band ratioing.International Journal of Remote Sensing, 8(2): pp. 137-155.

Graham, R. H., Salisbury, J. W., and Lenhoff, C. J. (1971). Visible and near infrared spectra ofminerals and rocks: III. Oxides and Hydroxides: Modern Geology, 2: pp. 195-205.

Jensen, J. R. (1986). Introductory digital image processing, a remote sensing perspective. Prentice-Hall, New Jersey: 379 p.

Rowan, L. C., Wetlaufer, P. H., Goetz, A. F. H., Billingsley, F. C., and Stewart, J. H. (1974).Discrimination of rock types and detection of hydrothermally altered areas in south-central

Nevada by use of computer-enhanced ERTS images: U.S.G.S. Prof. Paper 883, 35 p.

Thanks to Haluk Cetin for original ly developing th is exercise

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Lab8_ImageRatios