A mosaic image of the northern Great Bahama Bank was created from separate gray-scale Landsat images using photo-editing and image analysis software that is commercially available for desktop computers. Measurements of pixel gray levels (relative scale from 0 to 255 referred to as digital number, DN) on the mosaic image were compared to bank-top bathymetry (determined from a network of single-channel, high-resolution seismic profiles), bottom type (coarse sand, sandy mud, barren rock, or reef determined from seismic profiles and diver observations), and vegetative cover (presence and/or absence and relative density of the marine angiosperm Thalassia testudinum determined from diver observations). Results of these analyses indicate that bank-top bathymetry is a primary control on observed pixel DN, bottom type is a secondary control on pixel DN, and vegetative cover is a tertiary influence on pixel DN. Consequently, processing of the gray-scale Landsat mosaic with a directional gradient edge-detection filter generated a physiographic shaded relief image resembling bank-top bathymetric patterns related to submerged physiographic features across the platform. The visibility of submerged karst landforms, Pleistocene eolianite ridges, islands, and possible paleo-drainage patterns created during sea-level lowstands is significantly enhanced on processed images relative to the original mosaic. Bank-margin ooid shoals, platform interior sand bodies, reef edifices, and bidirectional sand waves are features resulting from Holocene carbonate deposition that are also more clearly visible on the new physiographic images. Combined with observational data (single-channel, high-resolution seismic profiles, bottom observations by SCUBA divers, sediment and rock cores) across the northern Great Bahama Bank, these physiographic images facilitate comprehension of areal relations among antecedent platform topography, physical processes, and ensuing depositional patterns during sea-level rise.