The spectral content of a scene is obtained using a step and stare technique, where the images are recorded one by one while rotating the camera or translating the camera between the frames. In each frame, the pixels are sampled at their uniquely filtered wavelength. There are, therefore, as many spectral points as there are pixels. For each frame during scanning, the scene will be slightly shifted with a new set of spectral samples associated with each pixel position. For $P$ number of shifted frames, each scene element will have been sampled at $P$ different wavelengths, i.e., the number of effective spectral channels equals the number of frames. To create a hypercube with a common preset spectral axis, the spectral vector is then interpolated to this axis for each image pixel. The finer spectral resolution and, therefore, the maximum number of independent spectral bands are governed by the inherent spectral bandwidth of the LVF and the density of registrations. The number of independent spectral bands is 40 for this filter, but due to the denser sampling needed at shorter wavelength, $\u223c100\u2009\u2009frames$ are needed to obtain full spectral resolution. This can be compared to a conventional push-broom system that, in this case, would require $\u223c5000\u2009\u2009frames$ with an array of $\u223c3000\u2009\u2009pixels$. In many applications, the spectral slope variation is rather slow and the spectrum can be sampled quite sparsely.^{23} For an image size of $M\xd7N$ pixels, the total dataset is $M\xd7N\xd7P$ data-points, which is a rather manageable size for large FPAs as well.