Detection and Segmentation of Underwater Archaeological Sites Surveyed with Stereo-Vision Platforms

Abstract

This paper proposes a method for automating detection and segmentation of archaeological structures in underwater environments. Underwater archaeologists have recently taken advantage of robotic or diver-operated stereo-vision platforms to survey and map submerged archaeological sites. From the acquired stereo images, 3D reconstruction can be performed to produce high-resolution photo-mosaic maps that are metrically accurate and contain information about depth. Archaeologists can then use these maps to manually outline or sketch features of interest, such as building plans of a submerged city. These features often contain large rocks that serve as the foundation to buildings and are arranged in patterns and geometric shapes that are characteristic of human-made structures. Our proposed method first detects these large rocks based on texture and depth information. Next, we exploit the characteristic geometry of human-made structures to identify foundation rocks arranged along lines to form walls. Then we propose to optimize the outlines of these walls by using the gradient of depth to seek the local minimum of the height from the seafloor to identify the ground plane at the base of the rocks. Finally, we output contours as geo-referenced layers for geographic information system (GIS) and architectural planning software. Experiments are based on a 2010 stereo reconstruction survey of Pavlopetri, a submerged city off the coast of Greece. The results provide a proof-of-concept for automating extraction of archaeological structure in underwater environments to produce geo-referenced contours for further analysis by underwater archaeologists.