Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

Meryl Mcdowell,Fred Kruse

doi:10.3390/rs8090757

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.

Highlights

Spectral remote sensing can be a powerful tool for the characterization of surface composition, when the area of interest is inaccessible, large, in need of repeated examination, or requires rapid evaluation
The majority of the previous work exploiting spectral data has focused on data from a single wavelength range, typically either the visible to near infrared (VNIR, ~0.4–1 μm), shortwave infrared (SWIR, ~1–2.5 μm), or longwave infrared
One this study study was was to to integrate integrate VNIR, VNIR, SWIR, SWIR, and and longwave infrared (LWIR)

Summary

Introduction

Spectral remote sensing can be a powerful tool for the characterization of surface composition, when the area of interest is inaccessible, large, in need of repeated examination, or requires rapid evaluation. The advantages of spectral remote sensing have been demonstrated for a wide range of materials and purposes, such as geological mapping, mining and geothermal exploration, vegetation and environmental studies, gas emission monitoring, urban mapping, and anomaly detection, e.g., [1,2,3,4,5]. Because the spectral features in each wavelength range differ based on materials’ physical and chemical properties, one range may be well suited for analysis of some materials but provide ambiguous or no information about others.

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Conclusion