Abstract
The Karhunen-Loeve transformation is applied to multispectral data for information extraction, SNR improvement, and data compression. When applied in the spectral dimension, the transform provides a set of uncorrelated principal component images very useful in automatic classification and human interpretation. Significant improvements in SNR and estimates of the noise variance are also shown to be possible in the spectral dimension. Data compression results using the transform on one-, two-, and three-dimensional blocks over three general types of terrain are presented.
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