Hyperspectral data cube

Figure 2 Data cube generation in mapping and imaging. The four-dimensional hyperspectral data cube contains the full spectral information, absorbance vs. wavenumber (v, cm ), for each x,y pixel from the imaged area, as is shown above. A horizontal slice through that cube contains a chemical image (e.g., band intensity at selected v for each x,y pixel of the image) as is shown below. The latter result could be obtained by Global Imaging (in which only the intensity distribution of a certain band over the imaged area would be recorded).

Figure 75-2 shows third-order data or a hyperspectral data cube where the spectral amplitude is measured at multiple frequencies (spectrum) with X and Y spatial dimensions included. Each plane in the figure represents the amplitude of the spectral signal at a single frequency for an X and Y coordinate spatial image. 

Mapping Historically, mapping [11] was the first method used to acquire hyperspectral data cubes, in particular with Raman spectroscopy and infrared (IR) microscopy. The image is created pixel by pixel in a step-and-acquire mode A spectrum is measured at one point of the sample, and then the sample moves to the next measurement position and another spectrum is acquired. The process is iterative for all positions in the area that define the image. 

Multivariate Image Analysis Strong and Weak Multiway Methods Strong and weak -way methods analyze 3D and 2D matrices, respectively. Hyperspectral data cube structure is described using chemometric vocabulary [17]. A two-way matrix, such as a classical NIR spectroscopy data set, has two modes object (matrix lines) and V variables (matrix columns). Hyperspectral data cubes possess two object modes and one variable mode and can be written as an OOV data array because of their two spatial directions. 

Pattern recognition can be classified according to several parameters. Below we discuss only the supervised/unsupervised dichotomy because it represents two different ways of analyzing hyperspectral data cubes. Unsupervised methods (cluster analysis) classify image pixels without calibration and with spectra only, in contrast to supervised classifications. Feature extraction methods [21] such as PCA or wavelet compression are often applied before cluster analysis. 

Figu re 5.9 (a) Brightfield image of a HeLa cell, attached to a Cap2 window, in buffer. Scale bar= 10 im (b) Raman spectral image, obtained via hierarchical cluster analysis (HCA) from a hyperspectral data cube collected atSOOnm spatial resolution (488 nm... 

Hyperspectral images are often displayed as data cubes, where two dimensions are the pixel coordinates (x and y) and the third dimension is the spectral one. In... 

Figure 56. Conceptualization of a three-dimensional hyperspectral image data cube...

Aside from the common processing techniques to obtain images more interpretable, there are other techniques that are more related to the dimension of hyperspectral data. If we consider the consistency of the hyperspectral data, we can easily understand the importance of finding a method which can transform the original data cube into one with reduced dimensionality and maintain, at the same time, as much information content as possible. In particular, dataset composed of hundreds of narrowband channels, besides storage and transmission, may cause problems in terms of complexity of processing and inversion phases. Therefore,... 

FIGURE 3 Schematic representation of the three common configurations in hyperspectral imaging devices and structure of the final data cube of dimensions (X X 7 X X). Slightly modified from [14] with permission of Springer. 

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