Shadows in remote-sensor images can yield marked errors in classification of riverine environments. We propose use of a modified shadow-removal algorithm as a preprocessing step for remote-sensing image classification of riverine landscapes. To accommodate characterization of spatially complex river features in the image, we investigate an illumination suppression-based shadow-removal algorithm, modified to include a user-defined tiling approach. We quantitatively evaluate the influence of shadow removal from aerial photography on classification accuracy as such studies are currently lacking. Experimental results demonstrate that this modified shadow-removal method significantly increases classification accuracy and improves detection of small river channels partially obscured by shadow.