Analysis of Change in Central Texas Using Image Differencing and Unsupervised Classification

Bonnie Brown, Daniel Unger, Judy Ann Rogers

Research output: Contribution to conferencePaper


An image differencing algorithm was applied to two Landsat MSS scenes in central Texas to assess its ability to identify change in the greater Austin, Texas metropolitan area. Near infrared data from a Landsat MSS scene acquired September 9, 1972 were subtracted from a Landsat MSS scene acquired August 24, 1990 to produce a difference image representing change in and around Austin, Texas covering a twenty year period. Results indicate that use of empirical analysis to visually identify change within a difference image is highly effective. Unsupervised classification of a difference image to identify change is dependent upon time requirements and the sensitivity of the classified image. While an unsupervised classification of a difference image with a small number of classes was shown to be time saving, it was determined to possess less subtle areas of change. Therefore, it became evident that the greater number of classes used resulted in a higher degree of identified subtle areas of change.

Original languageUndefined/Unknown
StatePublished - Jan 1 2000

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