Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.