Abstract
A selective variance reduction methodology is presented that reduces the band-to-band correlation observed in Landsat ETM imagery allowing a thematic-based de-correlation stretch. The bands 1 to 5, and band 7 are transformed to principal components (PCs). PC-1 and PC-2 account for the 94.7% of the total variance evident in images. In the current case study, band 3 and band 4 are selected to predict PC-1 and PC-2, respectively, through linear regression models. The PC-1 and PC-2 predicted image accounts for the 94.3% and 91.1%, respectively, of the total variance evident in the regression models. The bands 1 to 5 and 7 images are reconstructed from the two PC-1 and PC-2 residual images as well as PC-3 to PC-6 images. Thus, a thematic-based (band-dependent) decorrelation stretch of ETM imagery is achieved allowing the elimination of variance components that are related to spectral signature similarity of landcover types evident in Central Valley (California). The reconstructed imagery, a new higher order Landsat product, will assist image analysis, photo-interpretation, agricultural terrain analysis, mapping applications, and environmental monitoring at global level.
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