Because of the unique spectral characteristics of many alteration and rock-forming minerals, hyperspectral remote sensing can make a significant contribution to the field of exploration geology. Classical geologic mapping and mineral exploration utilize physical characteristics of rocks and soils such as mineralogy, weathering characteristics, geochemical signatures, and landforms to determine the nature and distribution of geologic units and to determine exploration targets of metals and industrial minerals. Subtle mineralogical differences—often important for distinguishing between rock formations or between barren ground and potential economic ore—are often difficult to map in the field. Hyperspectral remote sensing, the measurement of the Earth's surface in up to hundreds of spectral images, provides a unique means of remotely mapping mineralogy. A wide variety of hyperspectral data are now available, along with operational methods for qualitatively analyzing the data and producing mineral maps. This article illustrates the potential of these data and how they can be used as a tool to aid geological mapping and exploration.
Imaging spectrometry data or hyperspectral imagery (HSI) measure the reflectance or emissivity of the Earth's surface in many spectral bands, providing both spatial images and contiguous spectral coverage over selected spectral ranges. Hyperspectral data have been used for detailed mapping of materials for more than 15 years for geology. The solar spectral range, 0.4 to 2.5 micrometers, provides abundant information about many important Earth-surface minerals and research has proven the ability of hyperspectral systems to uniquely identify and map many of these minerals, even in sub-pixel amounts. Research has shown that imaging spectrometry principles and mapping capabilities are extensible to many other disciplines.
Imaging spectrometers acquire images in up to several hundred contiguous spectral bands simultaneously. Such data are well-suited to the identification of target materials having spectral reflectances distinguished primarily by narrow spectral features and are particularly important in discriminating between materials for which these features are limited to the same small region of the spectrum. One application requiring such data is the mapping of clay minerals. The reflectance spectra of these minerals have narrow absorption features (down to 10 nm in width) in the 2050 nm to 2350 nm range. In addition, the exact wavelength location and shape of a particular absorption feature may vary for an individual mineral type depending on the chemical composition of the sample in question. Such information can be extracted from imaging spectrometer data by an expert analyst.
There are an increasing number of airborne imaging spectrometers in operation—including the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), covering the spectral range between 400 nm and 2500 nm with 224 bands; Probe-1 with 128 bands over the range from 440 nm to 2543 nm; the Compact Airborne Spectrographic Imager (CASI), with up to 288 bands in the visible and near infrared part of the spectrum (400 nm to 1000 nm); and the Short wave infrared Full Spectrum Imager (SFSI), with 235 bands covering the spectral range from 1200 nm to 2450 nm.
One of the methods commonly used for the analysis of imaging spectrometer data is spectral unmixing. In general, a pixel observed by the remote sensing instrument consists of mixed materials. The hypothesis underlying linear unmixing is that the spectral radiance measured by the sensor consists of the radiances reflected by all of these materials, summed in proportion to the subpixel area covered by each material. To the degree that this hypothesis is valid, and that the "endmembers" are given by the reference spectra of each of the individual pure materials, and under the condition that these spectra are linearly independent, then in theory one can deduce the makeup of the target pixel by calculating the particular combination of the endmember spectra required to synthesize the target pixel spectrum. In practice there are a number of factors which act to confuse the issue. Some of these are sensor artifacts, sensor noise, atmospheric effects, solar incidence and terrain slope angles, surface roughness, and other radiometric influences.