We present a performance comparison of 4 feature descriptors for the task of feature matching in Panorama Stitching on images taken from architectural scenes and archaeological sites. Such scenes are generally characterized by structured objects that vary in their depth and large homogeneous regions. We test SIFT, LIOP, HRI and HRI-CSLTP on 4 different categories of images: well-structured with some depth variations, partially homogeneous with large depth variations, nearly homogeneous with a little amount of structural details and illumination-variant. These challenges test the distinctiveness and the intensity normalization schemes adopted by these descriptors. HRI-CSLTP and SIFT perform on par with each other and are better than the others on many of the test scenarios while LIOP performs well when the intensity changes are complex. The results of LIOP also show that the order computations of the pixels have to be made in a noise-resilient manner, especially in homogeneous regions. © Springer International Publishing Switzerland 2015.