skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Multispectral rock-type separation and classification.

Abstract

This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification, the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that providemore » reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.« less

Authors:
; ;  [1];  [1]
  1. (University of New Mexico, Albuquerque, NM)
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
957284
Report Number(s):
SAND2004-2980C
TRN: US201007%%561
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SPIE International Symposium on Optical Science and Technology held August 2-6, 2004 in Denver, CO.
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; BENCHMARKS; CLASSIFICATION; ROCKS; MINERALS; REMOTE SENSING; MULTISPECTRAL PHOTOGRAPHY; DATA ANALYSIS

Citation Formats

Moya, Mary M., Fogler, Robert Joseph, Paskaleva, Biliana, and Hayat, Majeed M.. Multispectral rock-type separation and classification.. United States: N. p., 2004. Web.
Moya, Mary M., Fogler, Robert Joseph, Paskaleva, Biliana, & Hayat, Majeed M.. Multispectral rock-type separation and classification.. United States.
Moya, Mary M., Fogler, Robert Joseph, Paskaleva, Biliana, and Hayat, Majeed M.. Tue . "Multispectral rock-type separation and classification.". United States. doi:.
@article{osti_957284,
title = {Multispectral rock-type separation and classification.},
author = {Moya, Mary M. and Fogler, Robert Joseph and Paskaleva, Biliana and Hayat, Majeed M.},
abstractNote = {This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification, the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that provide reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jun 01 00:00:00 EDT 2004},
month = {Tue Jun 01 00:00:00 EDT 2004}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: