Semester

Summer

Date of Graduation

2005

Document Type

Dissertation

Degree Type

PhD

College

Eberly College of Arts and Sciences

Department

Geology and Geography

Committee Chair

Timothy A Warner

Abstract

Visible, near infrared (VNIR), and short wave infrared (SWIR) hyperspectral and thermal infrared (TIR) multispectral remote sensing have become potential tool for geological mapping. In this dissertation, a series of studies were carried out to investigate the potential impact of combining VNIR/SWIR hyperspectral and TIR multispectral data for surface geological mapping. First, a series of simulated data sets based on the characteristics of hyperspectral AVIRIS and multispectral TIR MASTER sensors was created from surface reflectance and emissivity library spectra. Five common used classification methods including minimum distance, maximum likelihood, spectral angle mapper (SAM), spectral feature fitting (SFF), and binary encoding were applied to these simulated data sets to test the hypothesis. It was found that most methods applied to the combined data actually obtained improvement in overall accuracy of classification by comparison of the results to the simulated AVIRIS data or TIR MASTER alone. And some minerals and rocks with strong spectral features got a marked increase in classification accuracy. Second, two real data sets such as AVIRIS and MASTER of Cuprite, Nevada were used. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. The results of these classifications confirmed most findings from the simulated data analyses. Most silicate bearing rocks achieved great improvement in classification accuracy with the combined data. SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite sandstone which exhibit strong distinctive absorption features in the TIR region. SAM showed some advantages over SFF in dealing with multiple broad band TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have strong characteristic absorption features in the TIR region. One of the main objectives of these studies is to develop an automated classification algorithm which is effective for the analysis of VNIR/SWIR hyperspectral and TIR multispectral data. A rule based system was constructed to draw the strengths of disparate wavelength regions and different algorithms for geological mapping.

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