Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Abstract

[1] Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision-making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Application of remote sensing and GIS in mineral resource mapping-An overview

“…Sub pixel analysis methods calculate the quantity of reference materials in each pixel of an image and include tools such as linear spectral unmixing and matched filtering. Linear spectral unmixing exploits the theory that the reflectance spectrum of any (Clark et al, 2003). c. SAM classification map over Cuprite derived from SFSI 2 image.…”

Spectral properties of weathered and fresh rock surfaces in the Xiemisitai metallogenic belt, NW Xinjiang, China

“…In the 2000-2500 nm region, the reflectance change is lower and the two spectra tend to be similar to each other. Nevertheless, the short-wave bands at 2200 nm and 2350 nm have different spectral absorption characteristics, caused by hydroxyl, CO 2− 3 , and composite spectra among which the carbonate ion absorption bands are relatively stronger [6,44]. Meanwhile, the strong absorption at 2350 nm in CO 2− 3 is due to a higher content of sheet silicates on the fresh rock surfaces [16,42].…”

“…There are several important spectral library projects that have been established recently in this area of research such as the U.S. Geological Survey (USGS) spectral library [6], the Arizona State University (ASU), Thermal Emission Spectral Library [7], the ASTER spectral library [8], Specchio [4], the DLR spectral archive [9], and the globally distributed soil spectral library ICRAF-ISRIC (World Agroforestry CentreInternational Soil Reference and Information Centre) [10]. These spectral libraries provide a large amount of standard spectral data and remote sensing image extraction of spectral endmembers.…”

Universality of wavelet-based non-homogeneous hidden Markov chain model features for hyperspectral signatures

“…In practice, one commonly sees that class-wise features are identified by hand by expert practitioners to train rulebased systems such as the Tetracorder [3]. While this approach does provide features that are suitable for a variety of supervised learning problems, the process taxes practitioners with a significant amount of time in developing these features, and it will need to take place whenever a new class of data is added to the set of feasible observations.…”