High dynamic range imagery is widely used in remote sensing. With the widespread use of aerial digital cameras such as
the DMC, ADS40, RMK-D, and UltraCamD, high dynamic range imaging is generally expected for generating
minuteness orthophotos in digital aerial photogrammetry. However, high dynamic range images (12-bit, 4,096 gray
levels) are generally compressed into an 8-bit depth digital image (256 gray levels) owing to huge amount of data and
interface with peripherals such as monitors and printers. This means that a great deal of image data is eliminated from
the original image, and this introduces a new shadow problem. In particular, the influence of shadows in urban areas
causes serious problems when generating minuteness orthophotos and performing house detection. Therefore, shadow
problems can be solved by addressing the image compression problems.
There is a large body of literature on image compression techniques such as logarithmic compression and tone mapping
algorithms. However, logarithmic compression tends to cause loss of details in dark and/or light areas. Furthermore, the
logarithmic method intends to operate on the full scene. This means that high-resolution luminance information can not
be obtained. Even though tone mapping algorithms have the ability to operate over both full scene and local scene,
background knowledge is required. To resolve the shadow problem in digital aerial photogrammetry, shadow areas
should be recognized and corrected automatically without the loss of luminance information.
To this end, a practical shadow correction method using 12-bit real data acquired by DMC is investigated in this paper.