Hyperspectral Data Processing: Algorithm Design and Analysis

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Nonlinear Dimensionality Expansion to Multispectral Imagery

Hyperspectral imaging sensors have been around more than two decades. Interestingly, there is no cut-and-dried definition available in the literature to differentiate hyperspectral imagery from multispectral imagery. A general understanding of distinction between these two is that a hyperspectral image is acquired by hundreds of “contiguous” spectral channels/bands with very fine spectral resolution, while a multispectral image is collected by tens of “discrete” spectral channels/bands with low spectral resolution. If this interpretation is used, we then run into a dilemma, “how many spectral channels are sufficiently enough for a remotely sensed image to be called a hyperspectral image?” or “how fine the spectral resolution should be for a remote sensing image to be considered as a hyperspectral image?” For example, if we take a small set of hyperspectral band images with spectral resolution 10 nm, say five band images, to form a five-dimensional image cube, do we still consider this newly formed five-dimensional image cube as a hyperspectral image or simply a multispectral image? If we adopt the former definition based on the number of bands, this five-dimensional image cube should be viewed as a multispectral image. On the other hand, if we adopt the latter definition based on spectral resolution, the five-dimensional image cube should be considered as a hyperspectral image. So, which one is correct? Thus far, it ...