Semi-automated approaches to archaeological feature detection can be invaluable aids to the investigation of high resolution, large area archaeological prospection datasets. In order to obtain stable and reliable classification results, however, the application of pre-processing steps to digital terrain data is needed. This study examines semi-automated approaches to identification of archaeological features through a comparison of pixel-based and object-oriented data classification methods for archaeological feature detection in visualizations derived from high-resolution airborne laser scanning data. In doing so, openness is presented as a suitable visualization for feature detection due to its illumination-invariant representation of convexity and concavity in terrain data. The methodology of both pixel-based and object-oriented data classification approaches is described and applied to two datasets recorded over two archeological case study areas in Sweden and Austria. The diverse nature of the two datasets makes them ideal with regard to determining the robustness of the approaches discussed here. The obtained results are exported to a GIS environment and compared with manual visual interpretations and analyzed in terms of their accuracy. Therefore, this paper presents both a discussion regarding the merits of pixel- and object-based semi-automated classification strategies with regard to archaeological prospection data as well as practical examples of their implementation and results.