Presented at the Fourteenth International Conference on Applied Geologic Remote Sensing, Las Vegas, NV,

6-8 November 2000.

CURRENT RESEARCH ON SLOPE MOVEMENT IN MINES: 

USE OF HYPERSPECTRAL IMAGERY*

Edward L. McHugh,1 Jami M. Girard,1 Louis J. Denes,2 Peter Metes,2 and Charles Sabine3

1

National Institute for Occupational Safety and Health, Spokane, WA; 2Carnegie Mellon Research

Institute, Pittsburgh, PA; 3

Geopix, Sparks, NV 

ABSTRACT

The Spokane Research Laboratory, National Institute for Occupational Safety and Health,

is investigating various remote sensing technologies as possible tools to detect, monitor,

and mitigate hazardous situations in surface mines that could lead to catastrophic slope

failure. Promising technologies include a field-portable spectro-polarimetric imager and

a stationary interferometric radar device. 

Field tests were conducted of a prototype visible and near-infrared spectro-polarimetric

imager (SPI) built at the Carnegie Mellon Research Institute. The SPI employs an

acousto-optical tunable filter (AOTF) to control wavelength, a phase retarder to measure

polarization signatures, and a digital camera and computer to capture data. Images

obtained at the Mountain Pass and Castle Mountain mines in southeastern California

showed that the instrument is capable of obtaining hyperspectral images of highwalls,

outcrops, hand samples and drill core in the field. We have retrieved spectra from

Mountain Pass images that closely match those of bastnaesite and have had some success

in classifying images. Although radio frequency noise in the data limited spectral

classification of imagery, we project that careful alignment of the AOTF should minimize

the noise and greatly improve classification.

INTRODUCTION

The Spokane Research Laboratory, National Institute for Occupational Safety and Health does mine safety

and health research; a current project involves enhanced recognition of slope stability hazards at surface

mines. Since 1995, 33 miners have died in slope failure accidents at surface mines in the US. A principal

objective of the project is to improve the means for detecting conditions in open-pit mines that could lead

to catastrophic slope failure. A contributing factor in many highwall failures is the presence and geometry

of mechanically incompetent, clay-rich altered rock in pit walls. One element of the project is to test the

application of imaging spectrometers to help recognize these weakened altered rocks. Most alteration

minerals have diagnostic spectral absorption features in the short wave and mid-infrared that allow their

identification by analysis of the light they reflect. Instruments are commercially available that can analyze

light reflected from a single point, but an imaging system collects spectral reflectance data in a twodimensional array. A secondary application of high-resolution imagery, beyond just identifying altered
Figure 1. Spectro-polarimetric imager components.

rocks, would be to assist in compiling objective geologic maps of mine highwalls and other hazardous

slopes from a safe position.

Although spectral imaging is the focus of this paper, a second area of remote sensing research for mine

slope hazard detection is in the use of radar. High resolution radar imagery would complement spectral

imagery for detailed remote geologic mapping in surface mines. Low-cost interferometric radar technology

under development at Brigham Young University (BYU) shows promise for slope monitoring. Initial results

of tests by BYU engineers for the Canadian National Railroad have demonstrated a capability for detecting

rock falls and washouts along a right of way down to a few centimeters of displacement. Mine site tests of

a stationary monitoring radar are planned.

INSTRUMENTATION

Field trials were conducted at MolyCorp’s Mountain Pass Mine of a spectro-polarimetric imager (SPI)

developed by Dr. Louis Denes at Carnegie Mellon Research Institute (Denes and others, 1998). The

instrument is capable of obtaining multispectral images at visible and near-infrared wavelengths to generate

a data cube that can be processed by standard hyperspectral techniques. The compact and field-portable

instrument uses an acousto-optical tunable filter (AOTF) and an off-the-shelf, charge-coupled device (CCD)

camera, offering great flexibility for quickly obtaining images in a variety of environments (Figure 1). The

instrument was previously described by Gupta and others (1999) and Sabine and others (1999). Its

demonstrated use in a field environment could lead to development of ground-based imagers in different

spectral ranges, e.g., 1000 to 2500 nanometers (nm), that would help to distinguish and map other mineral

materials including clay-rich altered zones (see Clark and others, 1993).

Established hyperspectral imaging systems, such

as AVIRIS and HYDICE, are based on

conventional diffraction gratings and prisms with

two-dimensional detector arrays (Porter and

Enmark, 1987; Rickard and others, 1993). Such

systems operate in either whiskbroom or

pushbroom mode and collect data in hundreds of

narrow spatial bands to build an image. Designed

for airborne applications, they are generally heavy

and bulky. The AOTF-based imaging system

samples an entire two-dimensional scene at once

with no moving parts, making it compact and

robust. The programmable AOTF allows rapid

sequential or random tuning for different spectral

bands over a wide wavelength range.

The spectro-polarimetric imager consists of a tellurium dioxide (TeO2) AOTF crystal, radio frequency (RF)

electronics, image-forming optics, a charge-coupled device (CCD) camera, and an external host computer

with a frame grabber board and control and processing software. A summary of the imager specifications

are listed in table 1. During operation of the AOTF, an RF acoustic wave is applied to a birefringent TeO2

crystal through an attached piezoelectric transducer. This traveling acoustic wave sets up a diffraction

grating so that light incident on the crystal produces a diffracted beam with a shift corresponding to

wavelength resulting from the phase-matching condition. The wavelength of the diffracted light is selected
Parameter Value

Spectral range 450-1000 nm

Resolution 10 @ 600 nm

Switching rate 30 microsec/band

Retarder range 400-1800 nm

AOTF aperture 15×15 mm

Rf range 25-60 MHz

Rf power <1 W

Field of view (adjustable) 1.6 to 16 Deg

 Table 1. Specifications by tuning the applied RF frequency. The

center wavelength of the optical passband in

the AOTF is inversely proportional to the

applied RF and can be programmed in the host

computer for very rapid selection of desired

spectral and polarization parameters. 

Light entering the instrument first passes

through the acceptance optics, which collimate

the light before it passes through a phase

retarder and the AOTF. Active zoom control

on the acceptance optics provide a field of

view adjustable from 1.6L to 16L. The liquid

crystal variable phase retarder provides optional polarimetric control for the imaging system. Varying the

voltage applied to the retarder controls the amount of phase delay between the components, changing the

polarization state of the incoming light. The AOTF acts as an analyzer by transmitting only vertically

polarized light. For natural scenes in the visible spectrum, polarized light is primarily horizontal in

orientation. By varying retardation, polarization signatures of points in the scene including the polarization

angle and magnitude are transmitted to the CCD camera.

Software in the host computer sets the spectral range, spectral bandwidth, and polarization parameters for

data collection. Two control modes can be used at 30 microseconds per spectral band over wavelengths of

450 to 1000 nm. In sweep mode, the AOTF sequences through the full spectral range of the instrument,

collecting the maximum amount of information in a scene. In switching mode, the instrument alternates

between a small number of parameter settings for quick collection of specific bands for the sampled scene.

The switching mode can be programmed to obtain real-time band ratio images.

MOUNTAIN PASS DATA

The Mountain Pass Mine, which lies about 60 miles southwest of Las Vegas, Nevada, was selected because

the diagnostic spectral features of its rare-earth mineral assemblage are in the spectral range of the present

instrument. Data were collected at the mine in July 1999. Images were also collected at the nearby Castle

Mountain gold mine, operated by Viceroy Gold Corp., for comparison of images, spectra, and methodology.

Additional images were collected in the Carnegie Mellon laboratory in December 1999 and June 2000.

These images of hand specimens under artificial light sources were used as an aid in calibration and

evaluation of the instrument. Thin sections were made of both ore and host rocks to assist in interpreting

spectral images.

The Mountain Pass deposits occur as carbonatite veins associated with potassium-rich igneous rocks that

intrude a block of Precambrian metamorphic rocks. The carbonatites, apparently of magmatic origin,

consist mainly of calcite, dolomite, ankerite, and siderite. Bastnaesite, a fluorocarbonate of lanthanide

series metals and the principle rare-earth bearing mineral present, constitutes 5 to 15 percent of much of the

orebody and locally exceeds 60 percent (Olson and others, 1954). Rare-earth elements of the lanthanide

series (cerium, lanthanum, neodymium, europium and others) produced at the Mountain Pass Mine are used

in petroleum and pollution control catalysts, specialty glasses and magnets, and television and computer
Figure 2. Low-level AVIRIS scene (October 1999)

showing ground-based image collection sites.

Figure 3. SPI images showing (a) drill core; (b) vegetation and

outcrop; (c) hand samples; and (d) wall in main pit.

monitor phosphors. Diagnostic spectral

characteristics of the rare-earth assemblage

correspond to published spectra of neodymium

oxide, with at least eight distinctive absorption

features between 450 and 1000 nm. Spectral

reflectance data for the deposit were published by

Rowan and others (1986); work with AVIRIS data

for the deposit was described by Kingston (1993)

and Rowan and others (1996).

Twelve multispectral images, including pit

highwalls, outcrops, drill core from the ore zone,

and hand samples, were collected at the Mountain

Pass Mine. Collection sites are shown in Figure 2;

example images are in Figure 3. The data consist

of 47 bands from 480 to 940 nm in 10 nm intervals

each collected at four polarizations. For each

scene, original raw data comprising 190 bitmap

files were compiled into data cubes using ENVI

software. The default 2% stretch was turned off

before compilation and some files were spatially subset to reduce registration errors caused by wind-induced

camera shake during data collection.

Original data sets consisted of 640x480 pixel images; the images were cropped to eliminate dark margins

in some scenes. Each analyzed scene contained white and black cards that were used for data correction

and normalization. Mean values from a region of interest in the black card were used to correct for path

radiance using ENVI’s dark subtraction routine. A region of interest in the white card was used to

normalize the data to relative reflectance using ENVI’s flat field calibration technique.

Reflectance spectra of Mountain Pass

rare-earth ore were measured for this

study with an ASD full-range

spectrometer at 1-nm resolution and

resampled to 10-nm resolution. These

profiles had very good agreement with

U.S. Geological Survey library profiles

for neodymium oxide (Clark and others,

1993) and were used to evaluate the SPI

images. The most useful prominent

features occur at approximately 740 nm,

800 nm, and 870 nm.

Data were compiled into image cubes and

basic analysis was done for each of the

scenes, but this paper focuses on images

from the west highwall of the main pit at

Mountain Pass. The area of the highwall
Figure 4. West wall of the Mountain Pass Mine main pit, showing

the area of the spectral image in figure 5.

Figure 5. Original polarized image at 600 nm and 0L

polarization.

included in this data set is shown in

Figures 4 and 5; the dashed line shows

the approximate contact of the ore zone

(lower portion) with host rocks (upper

portion).

Original images (640x480 pixels, see

Figure 5) were cropped at the top, left,

and right sides, resulting in a 622x388

pixel, 47-band data cube. Planepolarized images showed minor

registration errors from camera shake

during data acquisition, and elevated

random noise at the high and low ends of

the spectral range. A second artifact in

the images is a set of near-vertical to

diagonal light and dark stripes resulting

from RF noise at the AOTF crystal/transducer boundary, due in part to misalignment caused by accidental

jarring during transport. ENVI band math scripts generated an averaged image set by combining all four

polarization images into a single data cube. Although some spatial resolution was lost in the averaged data,

the combined images showed significant improvement in random noise distribution. Resulting spectral

profiles were also smoothed in the combination. Only minor improvement was shown in the RF striping

noise component. The discussion that follows relates to the combined polarization image set. Dark

subtraction was done with a 276-pixel black card region of interest, and normalization to relative reflectance

was done with a 231-pixel white card region of interest.

A sampling of single pixels from the resulting data cube showed numerous spectral profiles that are related

to the expected rare-earth mineral assemblage. SPI spectra in Figure 6, offset 4 percent of full scale for

clarity, show a range of correlation with the

spectra measured from Mountain Pass

bastnaesite ore with the ASD spectrometer.

Some of the profiles match nearly all the

characteristic absorption features for the ore

(e.g., pixel x:108, y:77); several show the

asymmetry at the 790-800-nm feature.

IMAGE CLASSIFICATION

A first classification of the normalized images,

using ENVI’s Spectral Angle Mapper (SAM)

at the default spectral angle (0.10 radians),

was based on two end members defined as

regions of interest (roi) for presumed “ore”

and “host” portions of the image. The result

was quite reasonable and the size of the roi

polygons had little effect on the outcome. Roi

ranging from 60 to more than 2500 pixels
Figure 6. Sampling of spectra from the main pit west wall

that correlate with bastnaesite.

Figure 7. SAM classification on the west wall image

following a cross-track illumination correction; white areas

classified as ore.

returned approximately the same result leaving

about 20% of the image unclassified. A crosstrack illumination correction (multiplicative, two

polynomials) was applied to the original images to

reduce the effect of RF striping from the AOTF.

The correction reduced near-vertical striping and

an apparent x-axis gradient in the original images.

SAM classification of the result included larger

areas of ore end-member correlation in the left

side of the image, areas that were left unclassified

without the cross-track correction (Figure 7). The

lower portion of the image is predominantly ore;

the patchy isolated classified polygons in nearvertical stripes are likely associated with RF

noise. 

A second strategy applied similar classification

methods to the west wall image after applying

ENVI’s Minimum Noise Fraction (MNF)

transformation, using eight to eleven transformed

bands. The transformation was useful for

reducing random noise but was generally

unsuccessful in reducing the RF striping. Several

supervised classification methods and settings

with the transformed images showed no particular

improvement over that shown in Figure 7.

ENVI’s Pixel Purity IndexTM function applied to

the MNF transformed data (1200 iterations and a

threshold factor of 3) highlighted an endmember

with a profile that closely matched most of the

neodymium peaks in position and amplitude.

However, classifications based on this endmember

profile identified only very small areas within the

scene. Profiles for other apparent endmembers

had no features that relate to rare earths.

A third strategy for distinguishing ore-bearing

parts of the image made use of ENVI’s matched

filtering routines for partial unmixing to find a

predetermined endmember. The resampled ASD

spectral profile for bastnaesite ore was used as the

known endmember. The resulting classification,

shown in Figure 8, was relatively successful in

identifying ore in the lower portion of the image.

Again, vertical striping in the image relates to the

RF noise.
Figure 8. Matched filtering classification, white areas

correspond to bastnaesite spectrum.

CONCLUSIONS

The field tests were designed to evaluate the use of high resolution imagery to remotely characterize the

mineral composition of pit slopes. The SPI visible/near infrared images clearly illustrate the capabilities

of the method to collect a multispectral image and discriminate materials within the image, validating the

concept of simplified spectral imaging for such purposes. To apply the AOTF-based technology to more

common geologic materials, the instrument needs to extend further into the infrared. Images in the 2 to 2.5

micrometer part of the infrared spectrum, where spectral features for clays are more distinctive, would be

more useful for mapping altered zones in a highwall.

The field test data allowed isolation of spectral profiles that correlate with rare-earth elements expected at

the site. However, the signal-to-noise ratio in the data limited detailed classification of the images; spectral

features within the profiles may be broader and

shifted from standard library positions. A nextgeneration instrument will improve the signal-tonoise ratio and optimize calibration for more

reliable profile matching. Additional work needs

to be done on making use of the polarization

content of the AOTF images.

Where detection and recognition may require only

a limited set of specific spectral bands, depending

on the material sampled and backgrounds, an

AOTF allows high-speed spectral selectivity with

high resolution. Such capability greatly reduces

the amount of data collection and processing

required. The instrument can be used in the field

with minimal maintenance or special handling; its

demonstrated use is a first step in development of

imagers for a wide range of geologic materials.

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