- Hotspots and vignetting: these are common in scanned aerial photography, where the sun's reflection causes a bright spots. Vignetting is where a darkening occurs towards the corners of the image. See here for a good vignetting description.
- Flightlines of imagery flown on different days or different times of the day. Some mosaicking jobs include images that were flown at either different times of the day, or on different days (with different weather conditions). This can be a difficult problem to deal with because the imagery of the adjoining flightlines may be completely different in terms of the sun angle, brightness level, cloud coverage, and so forth.
- Brightness problems. Another issue is when, usually due to on-board sensor settings, one color band's brightness level is either too high or too low. For example, the imagery looks "blue-ish" because the blue band brightness level is skewed. Panchromatic imagery may also be too dark or light and need adjustment. It is also possible for there to be brightness variation within on image.
For aerial photography, one tool that can be used to alleviate the above problems is a process known as dodging. While there may be other problems, such as atmospheric issues associated with satellite imagery, these may require the use of other tools.
So how can dodging help?
Dodging basically attempts to create a uniform spectral intensity within and across images. Dodging, in the traditional photography sense of the word, is about reducing the exposure in a portion of the image in order to make that portion appear lighter. In photogrammetric processing this completely digital process. There are numerous programs available to perform dodging, and they often share techniques.
From ERDAS we offer dodging capabilities in a few different applications, namely Leica MosaicPro and ImageEqualizer. Both of these applications use a dodging algorithm originally developed by LH Systems for use with the DSW scanner line (implemented in a program called Fastdodge).
In these applications, the processes for statistics generation are similar:
A set of statistics are calculated (e.g. mean, standard deviation), which are based on certain user-defined parameters. These include grid size, skip percentage, and the minification layer (aka pyramid layer) for the process to run on. Of these, both the grid size and minification layer can be quite critical for success.
- Grid size defines the number of grid tiles in x and y for statistics to run on. For example, a grid size of 10 will result in 100 grid tiles (10 in x, 10 in y). Depending on the scene, this can have a big affect. For example, frames with relatively contiguous radiometry can use small grid sizes. "Complex" scenes, such as a frame containing water, urban, and rural/vegatated areas, usually require a higher grid size/density to prevent one radiometrically contiguous area from affecting another area. For example, consider a patch of dark green vegetation surrounded by say, lighter colored grain crops. If the grid size is significantly larger than the dark green patch, the output (depending on the constraints applied) will wash out the dark green because of the statistical influence of the DN values of the lighter colored grains. However, sometimes the problem outlined in this example can be impossible to avoid! This is why dodging is both an science and an art....
- "Skip percentage" is generally referred to as an "edge trimmer" (e.g. the percentage of the image edge that will be ignored during stats generation). It is important not to confuse this with "skip factor", which generally refers to a certain factor of pixels to "skip" when calculating stats. Using a high skip factor can have the same effect as using a lower-resolution pyramid layer.