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Land use classification using texture information in ERTS-A MSS imageryThe author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.
Document ID
19730011692
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Haralick, R. M. (Kansas Univ. Center for Research, Inc. Lawrence, KS, United States)
Shanmugam, K. S. (Kansas Univ. Center for Research, Inc. Lawrence, KS, United States)
Bosley, R. (Kansas Univ. Center for Research, Inc. Lawrence, KS, United States)
Date Acquired
September 2, 2013
Publication Date
January 1, 1973
Subject Category
Geophysics
Report/Patent Number
NASA-CR-131261
E73-10461
TR-2262-1
Accession Number
73N20419
Funding Number(s)
CONTRACT_GRANT: NAS5-21822
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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