Automated Cartographic Feature Attribution Using Panchromatic and Hyperspectral Imagery

David M. McKeown, Jr., Chris McGlone, Steven Douglas Cochran, Wilson A. Harvey, Jefferey A. Shufelt, and Daniel Yocum ,

Technical Report CMU-CS-98-161, Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, January 1996. 22 pages.

Abstract

This technical report describes research in the automated analysis of hyperspectral imagery (HYDICE). The goal of our research is to investigate issues in automatic surface material mapping using 210 channel image data from an airborne scanner sensitive from the visible through shortwave infrared wavelengths. Research issues addressed include sensor modeling, geometric correction and positioning, material classification experiments using two different interpretation models, and the fusion of geometric information from high resolution panchromatic imagery. Evaluation results are presented for building and road detection and attribution. The use of the resulting material classification maps for visual simulation is also presented.

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