Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

Abstract

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region.

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

“…The endmember spectra of several classes have decreasing emissivity from the 8.18 µm band to an emissivity minimum at 9-10 µm, which is consistent with a variety of materials, and are commonly mapped in similar areas (e.g., Classes 13,[17][18][19][20][21][22][23][24][25]. Their modeled distribution is sparsely scattered throughout much of the area but concentrated in the Early Proterozoic metamorphic and intrusive units, the small Jurassic intrusion in the southwestern Clark Mountains, and specific, relatively small locations within the siliciclastic units (Figure 10b).…”

“…Some of the work has been limited by a single thermal band [16], multispectral (i.e., a few spectral bands, 1-10) VNIR-SWIR imagery [17,18], or no inclusion of VNIR bands [19]. A few studies have been complicated by decreased accuracy for some classes dependent on spectral character and classification algorithm employed [20]. Many of the analyses have used per pixel classification requiring training data, e.g., [21] or extensive independent knowledge of the region to be characterized [22,23].…”

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

“…Lastly, the whole process was started over again as the new generation chromosomes were tested for their fitness scores. The spectral angle mapper classifier (SAM) is one of the most popular classification techniques for hyperspectral data [83,84]. First, the reflectance of each pixel is coded as n-dimensional vectors.…”

Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

“…The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Thermal Infrared Multispectral Scanner (TIMS) sensors have demonstrated the utility of TIR data to discriminate a wide range of minerals, especially silicates, as well as proving useful for lithological mapping (e.g. Chen et al, 2007;Rogge et al, 2009;Haselwimmer et al, 2010Haselwimmer et al, , 2011Salvatore et al, 2014); however, these satellite platforms are limited by their coarse spatial and spectral resolution.…”