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A comparison of Landsat point and rectangular field training sets for land-use classificationRectangular training fields of homogeneous spectroreflectance are commonly used in supervised pattern recognition efforts. Trial image classification with manually selected training sets gives irregular and misleading results due to statistical bias. A self-verifying, grid-sampled training point approach is proposed as a more statistically valid feature extraction technique. A systematic pixel sampling network of every ninth row and ninth column efficiently replaced the full image scene with smaller statistical vectors which preserved the necessary characteristics for classification. The composite second- and third-order average classification accuracy of 50.1 percent for 331,776 pixels in the full image substantially agreed with the 51 percent value predicted by the grid-sampled, 4,100-point training set.
Document ID
19860035962
Document Type
Conference Paper
Authors
Tom, C. H. (Hughes Aircraft Co. Englewood, CO, United States)
Miller, L. D. (Nebraska Remote Sensing Center Lincoln, United States)
Date Acquired
August 12, 2013
Publication Date
January 1, 1984
Subject Category
EARTH RESOURCES AND REMOTE SENSING
Distribution Limits
Public
Copyright
Other