Exploration of integrated visible to near-, shortwave-, and longwave-infrared (full range) hyperspectral data analysis

Cone, Shelli R.; Kruse, Fred A.; McDowell, M. L.

doi:10.1117/12.2086670

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…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).…”

Section: Remote Sens 2016 8 757mentioning

confidence: 68%

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Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

McDowell

Kruse

2016

Remote Sensing

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Abstract:We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR,~0.4-1 µm), shortwave infrared (SWIR,~1-2.5 µm), and longwave infrared (LWIR,~8-13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach.

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Section: Remote Sens 2016 8 757mentioning

confidence: 68%

“…Methods incorporating sub-pixel mixing are likely more realistic interpretations of remote sensing data, but these may still require outside knowledge of the mixing components, e.g., [24]. The most recent studies introduce hyperspectral (i.e., numerous narrow spectral bands) LWIR data into the full-range integrated analysis [23,25,26].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

McDowell

Kruse

2016

Remote Sensing

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“…The results summarized here compare spectral LWIR mapping results for MSI versus HSI data for a site near Mountain Pass, California. This effort is part of ongoing full-range (combined and integrated visible-to-near infrared [VNIR] shortwave infrared [SWIR], and LWIR) remote sensing analysis efforts at the Naval Postgraduate School [21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

Analysis of multispectral and hyperspectral longwave infrared (LWIR) data for geologic mapping

Kruse

McDowell²

2015

SPIE Proceedings

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Multispectral MODIS/ASTER Airborne Simulator (MASTER) data and Hyperspectral Thermal Emission Spectrometer (HyTES) data covering the 8 -12 m spectral range (longwave infrared or LWIR) were analyzed for an area near Mountain Pass, California. Decorrelation stretched images were initially used to highlight spectral differences between geologic materials. Both datasets were atmospherically corrected using the ISAC method, and the Normalized Emissivity approach was used to separate temperature and emissivity. The MASTER data had 10 LWIR spectral bands and approximately 35-meter spatial resolution and covered a larger area than the HyTES data, which were collected with 256 narrow (approximately 17nm-wide) spectral bands at approximately 2.3-meter spatial resolution. Spectra for key spatially-coherent, spectrally-determined geologic units for overlap areas were overlain and visually compared to determine similarities and differences. Endmember spectra were extracted from both datasets using n-dimensional scatterplotting and compared to emissivity spectral libraries for identification. Endmember distributions and abundances were then mapped using Mixture-Tuned Matched Filtering (MTMF), a partial unmixing approach. Multispectral results demonstrate separation of silica-rich vs non-silicate materials, with distinct mapping of carbonate areas and general correspondence to the regional geology. Hyperspectral results illustrate refined mapping of silicates with distinction between similar units based on the position, character, and shape of high resolution emission minima near 9 m. Calcite and dolomite were separated, identified, and mapped using HyTES based on a shift of the main carbonate emissivity minimum from approximately 11.3 to 11.2 m respectively. Both datasets demonstrate the utility of LWIR spectral remote sensing for geologic mapping.

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“…Endmember occurrence and abundances were then mapped for each dataset individually and in combination using partial unmixing [2]. Individual wavelength range results were also combined and integrated utilizing a variety of clustering and classification approaches [4][5][6].…”

Section: Approach and Methodsmentioning

confidence: 99%