Breast cancer is the second leading cause of death in women in the United States due to cancer. Early detection of breast cancerous regions will aid the diagnosis, staging, and treatment of breast cancer. Optical coherence tomography (OCT), a non-invasive imaging modality with high resolution, has been widely used to visualize various tissue types within the human breast and has demonstrated great potential for assessing tumor margins. Imaging large resected samples with a fast imaging speed can be accomplished by under-sampling in the spatial domain, resulting in a large image scale. However, it is unclear whether there is an impact on the ability to classify tissue types based on the selected imaging scale. Our objective is to evaluate how the scale at which the images are acquired impacts texture features and the accuracy of an automated classification algorithm. To this end, we present a comparative study of texture features in OCT images at two image scales for human breast tissue classification. Texture features and attenuation coefficients were inputs to a statistical classification model, relevance vector machine. The automated classification results from the two image scales were compared. We found that more informative tissue features are preserved in small image scale and accordingly, small image scale leads to more accurate tissue type classification.