In this paper we present a technique to accurately build a 3D hyperspectral image cube from a 2D imager overlaid with a wedge filter with up to hundreds of spectral bands, providing time-multiplexed data through scanning. The correctness of the spectral curve of each pixel in the physical scene, being the combination of its spectral information captured over different time stamps, is directly related to the alignment accuracy and scanning sensitivity. To overcome the accumulated alignment errors from scanning inaccuracies, frequency- dependent scaling from lens, spectral band separations and the imager’s spectral filter technology limitations, we have designed a new image alignment algorithm based on Random Sample Consensus (RANSAC) model fitting. It estimates many mechanical and optical system model parameters with image feature matching over the spectral bands, ensuring high immunity against the spectral reflectance variations, noise, motion-blur, blur etc. The estimated system model parameters are used to align the images captured over different bands in the 3D hypercube, reducing the average alignment error to 0.5 pixels, much below the alignment error obtained with state-of-the-art techniques. The image feature correspondences between the images in different bands of the same object are consistently produced, resulting in a hardware-software co-designed hyperspectral imager system, conciliating high quality and correct spectral curve responses with low-cost.
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