Tie point generation in hyperspectral cubes for orientation with polynomial models

This paper presents a technique for tie point generation in hyperspectral images collected by a camera with time-sequential principle for band acquisition (i.e., non-synchronized bands). In mobile applications, each band is acquired at a different time, which generates different camera positions and attitude angles. Due to the large number of bands, a bundle adjustment with polynomial models can be applied to sample bands and then, EOP of other bands are interpolated. The determination of homologue points in all sample bands is required to ensure geometric robustness. A procedure was developed to extract tie points from a reference band which were then transferred to the other sample bands. The technique uses a Helmert geometric transformation combined with majority voting to estimate point transfer functions, followed by area-based matching. Experiments with image orientation were conducted to apply and assess the technique. The tests showed an increase in height discrepancy when tie points are not located in all sample bands highlighting the relevance of the proposed filtering. The accuracy of the technique achieved less than 1 GSD in planimetry and 2 GSD in altimetry using the tie points with the maximum number of rays. Thus, the polynomial approach enables interpolation of other bands according to the parameters of the polynomial function.