One of the major driving forces behind digital photogrammetric systems is the continued drop in the cost of digital storage systems. However, terrestrial remote sensing systems continue to generate enormous volumes of data due to smaller pixels, larger coverage, and increased multispectral and multitemporal possibilities. Sophisticated compression algorithms have been developed but reduced visual quality of their output, which impedes object identification, and resultant geometric deformation have been limiting factors in employing compression. Compression and decompression time is also an issue but of less importance due to off-line possibilities. Two typical image blocks have been selected, one sub-block from a SPOT image and the other is an image of industrial targets taken with an off-the-shelf CCD. Three common compression algorithms have been chosen: JPEG, Wavelet, and Fractal. The images are run through the compression/decompression cycle, with parameter chosen to cover the whole range of available compression ratios. Points are identified on these images and their locations are compared against those in the originals. These results are presented to assist choice of compression facilities after considerations on metric quality against storage availability. Fractals offer the best visual quality but JPEG, closely followed by wavelets, imposes less geometric defects. JPEG seems to offer the best all-around performance when you consider geometric and visual quality, and compression/decompression speed.