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MATLAB Graphics and Data Visualization Cookbook by Swapnonil Banerjee, Nivedita Majumdar

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Fusing hyperspectral data

Spectral imaging collects information from across the electromagnetic spectrum including and extending beyond the visible ranges. Objects leave patterns across the spectrum (their spectral signatures), that enable their identification. For example, a spectral signature for oil helps mineralogists find new oil fields. The reason for imaging across multiple bands is that not all features are visible in all bands.

These large volumes of data collected demand appropriate methods for scanning and interpretation. Commonly, data is first transformed using a method such as the Principal Component Analysis and then the three most significant components are used as the RGB channel data to create false color images.

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