In addition to image coverage, the other key consideration in the
planning of any survey is how the texture of the target will resolve
in individual photographs. Automated feature matching relies upon
the ability of computer algorithms to identify unique
corresponding features in overlapping photos. For good results, any
effects that reduce textural variability within images or increase
feature variability between images should be minimized during
acquisition. Common issues preventing algorithms from resolving
coincident points include homogeneous surface texture, changes in
the target, and changes in illumination. The latter two have the
same effect of making a unique feature appear differently between
images, although both require different strategies for reducing the
deleterious effect on model construction. Poor or variable image
texture is often due to surface reflections, flat surfaces with little
textural variation, and the occurrence of deep shadows. The target
itself may appear to change between images due to wind shifting
vegetation, or the movement of people and vehicles. Changes in
illumination can result from accidental shading by the photographer,
changes in the sun position, or filtering by clouds. Strategies
for circumnavigating these issues in structural studies are outlined
further in Section 5.2.