Geostatistical incorporation of spatial coordinates into supervised classification of hyperspectral data |
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| Authors: | P Goovaerts |
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| Institution: | (1) Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, MI 48109-2125, USA (e-mail: goovaert@engin.umich.edu), US |
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| Abstract: | This paper presents a methodology to incorporate both hyperspectral properties and spatial coordinates of pixels in maximum
likelihood classification. Indicator kriging of ground data is used to estimate, for each pixel, the prior probabilities of
occurrence of classes which are then combined with spectral-based probabilities within a Bayesian framework. In the case study
(mapping of in-stream habitats), accounting for spatial coordinates increases the overall producer's accuracy from 85.8% to
93.8%, while the Kappa statistic rises from 0.74 to 0.88. Best results are obtained using only indicator kriging-based probabilities,
with a stunning overall accuracy of 97.2%. Significant improvements are observed for environmentally important units, such
as pools (Kappa: 0.17 to 0.74) and eddy drop zones (Kappa: 0.65 to 0.87). The lack of benefit of using hyperspectral information
in the present study can be explained by the dense network of ground observations and the high spatial continuity of field
classification which might be spurious.
Received: 12 April 2001 / Accepted: 7 September 2001 |
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| Keywords: | : Hyperspectral streams supervised classification kriging |
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