Preliminary results on the analysis of HYDICE data for information fusion in cartographic feature extraction

Stephen J. Ford; Dirk Kalp; J. Chris McGlone; David M. McKeown Jr.

doi:10.1117/12.281054

5 August 1997 Preliminary results on the analysis of HYDICE data for information fusion in cartographic feature extraction

Stephen J. Ford, Dirk Kalp, J. Chris McGlone, David M. McKeown Jr.

Author Affiliations +

Proceedings Volume 3072, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision III; (1997) https://doi.org/10.1117/12.281054
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

This paper discusses ongoing research in the analysis of airborne hyperspectral imagery with application to cartographic feature extraction and surface material attribution. Preliminary results, based upon the processing and analysis of hyperspectral data acquired by the Naval Research Laboratory's (NRL) Hyperspectral Digital Imagery Collection Experiment (HYDICE) over Fort Hood, Texas in late 1995, are shown. Significant research issues in geopositioning, multisensor registration, spectral analysis, and surface material classification are discussed. The research goal is to measure the utility of hyperspectral imagery acquired with high spatial resolution (2 meter GSD) to support automated cartographic feature extraction. Our hypothesis is that the addition of a hyperspectral dataset, with spatial resolution comparable to panchromatic mapping imagery, enables opportunities to exploit the inherent spectral information of the hyperspectral imagery to aid in urban scene analysis for cartographic feature extraction and spatial database population. Test areas selected from the Fort Hood dataset will illustrate the process flow and serve to show current research results.

Citation Download Citation

Stephen J. Ford, Dirk Kalp, J. Chris McGlone, and David M. McKeown Jr. "Preliminary results on the analysis of HYDICE data for information fusion in cartographic feature extraction", Proc. SPIE 3072, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision III, (5 August 1997); https://doi.org/10.1117/12.281054

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