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Kriging in the Shadows: Geostatistical Interpolation for Remote SensingIt is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.
Document ID
19970010271
Acquisition Source
Ames Research Center
Document Type
Reprint (Version printed in journal)
Authors
Rossi, Richard E.
(Issaks and Co. Redwood City, CA United States)
Dungan, Jennifer L.
(Johnson Controls World Services Moffett Field, CA United States)
Beck, Louisa R.
(Johnson Controls World Services Moffett Field, CA United States)
Date Acquired
August 17, 2013
Publication Date
January 1, 1994
Publication Information
Publication: Remote Sens. Environ.
Publisher: Elsevier, Science Inc.
Volume: 49
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
NAS 1.15:112103
NASA-TM-112103
Accession Number
97N70695
Distribution Limits
Public
Copyright
Public Use Permitted.
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