Classification of Hyperspectral Data: A Comparative Study

Classification of Hyperspectral Data: A Comparative Study In general, the analysis of hyperspectral remote sensing data by means of pattern recognition and/or classification is known to be data dependent. Thus, conventional methods for classifications may not be applicable due to the large amount of data collection used to characterize hyperspectral data in terms of optimality and computational time. In this paper, efficient classification methods of hyperspectral data are presented. Hyperspectral signatures are then extracted for eight different sample types (bare soil, soybean, mixed weeds, combination of soybean and weeds, sicklepod, entireleaf morning glory, pitted morning glory, and common cocklebur) and used for observing their spectral properties for detection and/or classification. The hyperspectral data that are analyzed in this paper are point source data collected from handheld spectrometers. The resulting data classification is reported accordingly and a comparative study among the various methods is made to ascertain their applicability to discriminate between the various sample types. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Classification of Hyperspectral Data: A Comparative Study

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2004 by Kluwer Academic Publishers
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1023/B:PRAG.0000013619.63786.16
Publisher site
See Article on Publisher Site

Abstract

In general, the analysis of hyperspectral remote sensing data by means of pattern recognition and/or classification is known to be data dependent. Thus, conventional methods for classifications may not be applicable due to the large amount of data collection used to characterize hyperspectral data in terms of optimality and computational time. In this paper, efficient classification methods of hyperspectral data are presented. Hyperspectral signatures are then extracted for eight different sample types (bare soil, soybean, mixed weeds, combination of soybean and weeds, sicklepod, entireleaf morning glory, pitted morning glory, and common cocklebur) and used for observing their spectral properties for detection and/or classification. The hyperspectral data that are analyzed in this paper are point source data collected from handheld spectrometers. The resulting data classification is reported accordingly and a comparative study among the various methods is made to ascertain their applicability to discriminate between the various sample types.

Journal

Precision AgricultureSpringer Journals

Published: Oct 1, 2004

References

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