Temporal Change Enhancement in Multispectral Images Remotely Sensed from Satellites

University of Utah

The application of principal components analysis to multispectral satellite images is a routine way to present the data in false-color composite images. These composite images include a very high percentage of available information and have no correlation between the displayed colors. The transformation of multispectral image data into its principal components is also an effective way to separate image information from noise. This paper describes a procedure for temporal change enhancement which exploits both the decorrelation and noise isolation properties of the principal components transformation. Using simulated temporal change, this procedure was demonstrated to be more effective than the standard procedures described in the literature. Simulated temporal change as low as one percent of original pixel brightness was easily seen. Changes of less than one percent were difficult to simulate because of integer roundoff. However, a one percent change was sufficient to measure the relative effectiveness of each procedure considered.