IBM Journal of Research and Development · 1978 · 15 citations · 4 references
ArtsVisualizationColor TheoryDigital ImagingRemote SensingColor RepresentationDigital Image ProcessingImage ProcessingMultispectral ImagingPrincipal Component AnalysisRemote Sensing Image AnalysisAxis TransformationsData VariabilityMultispectral AnalysisMultispectral Image AnalysisMultispectral SensingPrincipal AxisPrincipal Components
When principal axis transformations are applied to multispectral scanner (MSS) data, the majority of data variability is shown to be contained in the first two or three components. This paper describes a method for generating a color composite picture from these components whereby most of the information collected by an MSS can be conveyed in a single color picture. The first component is found to be a weighted sum of image data from all channels, and therefore it is natural to associate the first component with brightness. To be consistent with this interpretation, only the first component of the transformed data is used to control the brightness of the color composite picture.
4