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Proceedings Paper

Study Of Sensor Spectral Responses And Data Processing Algorithms And Architectures For Onboard Feature Identification
Author(s): F. O. Huck; R. E. Davis; C. L. Fales; R. M. Aherron
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Paper Abstract

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced Earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MDS) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Paper Details

Date Published: 23 November 1982
PDF: 24 pages
Proc. SPIE 0345, Advanced Multispectral Remote Sensing Technology and Applications, (23 November 1982); doi: 10.1117/12.933770
Show Author Affiliations
F. O. Huck, NASA/Langley Research Center (United States)
R. E. Davis, NASA/Langley Research Center (United States)
C. L. Fales, NASA/Langley Research Center (United States)
R. M. Aherron, Information and Control Systems,Inc. (United States)


Published in SPIE Proceedings Vol. 0345:
Advanced Multispectral Remote Sensing Technology and Applications
Ken J. Ando, Editor(s)

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