The problem addressed in this paper is that of clustering image pixels into regions of homogenous geological texture. Future rovers on Mars will need to be able to intelligently select data collection targets. One goal of intelligent data selection for maximizing scientific return is to sample all distinct types of rocks that may be encountered. Different rock types often have a characteristic visual texture, thus visual texture is rich source of information for separating rocks into different types. Recent work on using texture to segment images has been very successful on images with homogenous textures such as mosaics of Brodatz textures and some natural scenes. The geologic history of a rock leads to irregular shapes and surface textures. As a result, the textures in our images are not as homogeneous as those in Brodatz mosaics. Our approach is to extract textural information by applying a bank of Gabor filters to the image. The resulting texture vectors are then clustered. Banks of filters constrain the relationships of the filter parameters both within a single filter and between filters. Often researchers have used parameter values that are thought to correspond to the human visual system, however the effects of adjusting these parameters have not been thoroughly studied. We systematically explore tradeoffs in the parameter space of the filter bank and quantify the effects of the takeoffs on the quality of the resulting clusters.