ENHANCEMENT OF HIGH SPECTRAL RESOLUTION REMOTE-SENSING DATA BY A NOISE-ADJUSTED PRINCIPAL COMPONENTS TRANSFORM

High spectral resolution remote-sensing data collected with a 64-channel scanner (Geophysical and Environmental Research) has been enhanced by a noise-dependent transform which is an analogue of the principal components transform. The data are first transformed so that the noise covariance matrix becomes the identity matrix, and then the principal components transform is applied. This transform is equivalent to the maximum noise fraction transform and is optimal in the sense that it maximizes the signal-to-noise ratio (SNR) in each successive transform component, just as the principal component transform maximizes the data variance in successive components. Application of this transform requires knowledge or an estimate of the noise covariance matrix of the data. With data from the Geophysical and Environmental Research 64-channel scanner, this was available from measurements of the signal from a low reflectance reference plate which was recorded interspersed with the data. The need for noise reduction in the data from this scanner was increased by the presence of a serious noise component in a few of the spectral bands. This noise has only been observed to this extent in data collected during an Australian survey mission in November/December 1987 and is not expected to be a permanent characteristic of the instrument. The effectiveness of this transform for noise removal is demonstrated in both the spatial and spectral domains. Results are presented that demonstrate the enhancement of geological mapping and detection of alteration mineralogy in data from the Pilbara region of Western Australia, including mapping of the occurrence of pyrophyllite over an extended area. © 1990 Canadian Crown Copyright