A microtopographic feature analysis-based LiDAR data processing approach for the identification of Chu tombs

Abstract

Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains.