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Proceedings Paper

Automated Segmentation Of Pseudoinvariant Features From Multispectral Imagery
Author(s): Carl Salvaggio; John R Schott
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Paper Abstract

An automated segmentation algorithm for the isolation of pseudoinvariant features was developed as a part of this study.1 This algorithm utilizes rate-of-change information from the thresholding process previously associated with the pseudoinvariant feature normalization technique demonstrated by Volchok and Schott, 1986.2 The segmentation algorithm was combined with the normalization technique and applied to the six reflective bands of the Landsat Thematic Mapper (TM) for both urban and rural scenes. The technique was also applied to color infrared high resolution U2 imagery. The accuracy and precision of the normalization results were evaluated. The combined techniques consistently produced normalization results with errors of approximately one to two reflectance units for both the rural and urban TM imagery as well as the visible bands of the high resolution air photo imagery. These results compare favorably with previous findings utilizing the manual segmentation technique while simultaneously eliminating user-to-user inconsistencies. Also developed as a result of this study was a quantitative metric for comparison of different normalization techniques.

Paper Details

Date Published: 8 June 1988
PDF: 10 pages
Proc. SPIE 0902, Three-Dimensional Imaging and Remote Sensing Imaging, (8 June 1988); doi: 10.1117/12.944772
Show Author Affiliations
Carl Salvaggio, Rochester Institute of Technology (United States)
John R Schott, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 0902:
Three-Dimensional Imaging and Remote Sensing Imaging
Woodrow E. Robbins, Editor(s)

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