Internet Archaeol 15. Armin Schmidt. Data processing

2.3 Data processing

Once remote sensing data are available in digital format, computer processing can be used to enhance small variations related to archaeological information. Most modern image manipulation software, even including some freeware packages (e.g. www.irfanview.com), contain image enhancement routines that can be applied to remote sensing images. It is important to remember that such processing may in some cases improve feature definition but cannot replace high-quality input data, for example aerial photographs taken with good cameras and films.

The most important processing of remote sensing data is their rectification and georeferencing. Based on control points identified in an image and on maps, photogrammetric calculations project the data onto a base map. To achieve high accuracy, topographical data must be considered in these calculations (Doneus 2001) and a number of software packages are available for aerial archaeology, most notably Aerial (Haigh 2000) and AirPhoto (Scollar 1998). As with all archaeological prospection techniques, interpretation of the original data is required and there is some discussion about whether interpretations should be carried out on the original oblique image and then rectified in the same way as the photograph (Palmer 2000), or whether interpretation diagrams could be based on rectified images alone. The former approach has the advantage that break lines and shadows can be identified more easily, while the latter method is less time consuming and allows interpretations to be based on actual feature shapes (e.g. otherwise distorted circular ditch systems).

The distortion of images due to undulating topography can be exploited for their three-dimensional analysis. If vertical remote sensing scenes (satellite- or airborne) are collected with considerable overlap, or oblique aerial photographs are taken from two close positions, pairs of such images can be used to view the results in three-dimensions with an optical stereoscope or to analyse them with a stereo-plotter. If the paired images are available in digital format they can be used to compute topographical models. However, most of the commercially implemented algorithms require considerable user input and information about camera position, direction and calibration, as well as ground control points. For many archaeological aerial photographs, such information is not easily available. Redfern et al. (1999) developed a new algorithm for individual archaeological monuments (i.e. only small areas of a photograph) that requires only limited user input. The accuracy of a resulting DTM is about 0.9m and it is hoped that further improvements will make this technique a cost-effective alternative to ground-based topographic recording.

While multispectral images are clearly desirable for feature classification (see above), the complexity of necessary sensors leads to coarser ground resolution than for panchromatic (i.e. 'black-and-white') data. To overcome this limitation it is possible to compute higher-resolution multispectral scenes using interpolation schemes that are guided by the panchromatic images with higher resolution. It is therefore necessary that the spatial matching of both datasets is carried out with very high accuracy. The interpolation algorithms are sophisticated and not yet widely available. However, with high-resolution multispectral images produced in this way, it will be possible to classify individual archaeological features based on their spectral characteristics (e.g. walls or ditches). In contrast to conventional remote sensing applications (e.g. determining agricultural crops or ground covers), it may be necessary to include information on the surrounding areas into the classification algorithms. This would help to identify, for example, wilted parch marks on grass and early ripened crop-marks in wheat both as signatures of buried walls, despite the different moisture levels in the respective vegetation and hence different spectral signatures.