Comparison of Machine Learning Pixel-Based Classifiers for Detecting Archaeological Ceramics

Abstract

Recent improvements in low-altitude remote sensors and image processing analysis can be utilised to support archaeological research. Over the last decade, the increased use of remote sensing sensors and their products for archaeological science and cultural heritage studies has been reported in the literature. Therefore, different spatial and spectral analysis datasets have been applied to recognise archaeological remains or map environmental changes over time. Recently, more thorough object detection approaches have been adopted by researchers for the automated detection of surface ceramics. In this study, we applied several supervised machine learning classifiers using red-green-blue (RGB) and multispectral high-resolution drone imageries over a simulated archaeological area to evaluate their performance towards semi-automatic surface ceramic detection. The overall results indicated that low-altitude remote sensing sensors and advanced image processing techniques can be innovative in archaeological research. Nevertheless, the study results also pointed out existing research limitations in the detection of surface ceramics, which affect the detection accuracy. The development of a novel, robust methodology aimed to address the “accuracy paradox” of imbalanced data samples for optimising archaeological surface ceramic detection. At the same time, this study attempted to fill a gap in the literature by blending AI methodologies for non-uniformly distributed classes. Indeed, detecting surface ceramics using RGB or multi-spectral drone imageries should be reconsidered as an ‘imbalanced data distribution’ problem. To address this paradox, novel approaches need to be developed.