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A 2DPCA-based method for automatic selection of hyperspectral image bands for color visualization

Hyperspectral imagery (HSI) is a relatively new technology capable of relaying intensity information gathered from both visible and non-visible ranges of the electromagnetic spectrum. HSI images can contain hundreds of bands, which present a problem when an image analyst must select the most relevant bands from such an image for visualization, particularly when the bands that are within the range of human vision are either not present or heavily distorted. It is proposed here that two-dimensional principal component analysis (2DPCA) can aid in the automatic selection of the bands from an HSI image that would best reflect visual information. The method requires neither prior knowledge of the image contents nor the association between spectral bands and their center wavelengths.