Space exploration could be significantly aided by combining the disciplines of machine learning and computer vision, but these disciplines need to be developed further for specific space-related applications to have merit. One of the applications for space exploration is the detection of certain structures designating areas of interest. This thesis demonstrates a method of structure-detecting that is applied to staircases. In addition to incorporating certain physical features, like other algorithms have done, the proposed algorithm (Step-1) also takes into account the spatial relation between these features, in order to increase its robustness. Looking at a staircase from the front, the distances between each step become warped, as they are further away from the observer. This exponential spatial distortion is known as a ’chirp’. Step-1 tries to match a chirp-waveform to every edge along a straight line randomly drawn through an image, and based on that match classify the image as containing a staircase or not. The random lines are then weighted based on their effectiveness using Adaboost, which are finally combined to obtain a final classification. The results show potential but there are still some issues to be addressed. However, once the algorithm has been upgraded it could aid space exploration by being applied to satellite images and autonomous rovers.