In the field of geophysics, huge volumes of information often need to be processed with complex and time-consuming algorithms to better understand the nature of the data at hand. A particularly useful instrument within a geophysicists toolbox is a set of decorrelating transforms. Such transforms play a key role in the acquisition and processing of satellite-gathered information, and notably in the processing of hyperspectral images. Satellite images have a substantial amount of redundancy that not only renders the true nature of certain events less perceivable to geophysicists but also poses an issue to satellite makers, who have to exploit this data redundancy in the design of compression algorithms due to the constraints of down-link channels. This issue is magnified for hyperspectral imaging sensors, which capture hundreds of visual representations of a given targeteach representation (called a component or a band) for a small range of the light spectrum. Although seldom alone, decorrelation transforms are often used to alleviate this situation by changing the original data space into a representation where redundancy is decreased and valuable information is more apparent.