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Hyperspectral vs Multispectral
The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography
Principal Component Analysis in Hyperspectral Processing
Principal components analysis or PCA essentially is a way of summarizing your data set. It's a transformation that gets applied to your data where it tries to find the key information out of it and summarize all of the variation within the image in a handful of basically new bands, which are called components. So this is a common technique used in hyperspectral processing in order to essentially get at the salient details and cut through a lot of the noise.
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