In many automated processes, the interpretation of aerial photos requires knowledge of certain object properties (e.g. spectral signature, texture, panchromatic reflection) in a quantified form. According to their structure (e.g. vegetation, land furrows), most natural terrain surfaces show reflection characteristics which depend to a high degree upon the direction of observation. In aerial
... [Show full abstract] photos this leads shadow-and-no-shadow-areas. Because of this vertical structure, the spectral signatures and the textures of the objects could change under various directions of observation.This paper studies the possibility of determining the directionally dependent reflection features of horizontal terrain areas—especially agricultural areas—on the basis of denstiess from black-and-white aerial photos. It is also possible to correct all densities in an aerial photo due to disturbing influences such as transmission by atmosphere, filter and lens and others such as atmospheric haze, light fall-off and non-uniform development. Advantage is taken of the fact that in a photo strip with sufficient longitudinal and side overlap, one and the same object is imaged into various image points. The calculation of directional reflectances is done with the help of two references areas with known reflection features. These reference ares must have a large difference in their reflectances. Apart from this no special demands are made (e.g. agricultural areas are possible). Practical tests of the method performed with aerial photos on a scale of 1: 15,000 yielded mean residual errors between 1.2% and 1.7% of the absolute directional reflectance. With an optimal application of the technique, residual errors of ±1% and better can be expected. This accuracy seems to be entirely adequate for such objects. Furthermore, some single problems are treated which are important for a quantitative photointerpretation.