Inshore marine litter detection using radiometric and geometric data of terrestrial laser scanners

The increasing inshore marine litters (IML) have been jeopardizing the coastal ecology and environment and have attracted widespread concerns. Nevertheless, the accurate detection and quantitative characterization of IML remain a challenge. In this study, a new method is proposed to automatically detect and extract the IML from terrestrial laser scanning (TLS) 3D point clouds. IML are progressively extracted from the surroundings through four major steps by jointly using the radiometric/intensity information and a series of derived geometric features. First, the intensity data are calibrated by a polynomial model for an initial segmentation according to the spectral differences between the IML and surroundings. Second, a new proposed model is used to calibrate the density data for a further discrimination based on the size discrepancies between the IML and surroundings. Third, a connectivity clustering algorithm is used to group the points into different clusters. Cluster geometric features in terms of the shapes and patterns (i.e., linearity, sizes, and verticality) are constructed to identify the IML. Fourth, a geometric self-repairing procedure is used to retrieve the misclassified IML points. An artificially-arranged scene on a bare mudflat and four natural scenes with different circumstances and IML categories are investigated to validate the proposed method. The overall accuracy and kappa coefficient of the proposed method are averagely 98% and 0.69, respectively. Compared with the classical methods, the proposed method shows good robustness performance in different natural scenes with varied IML categories, vegetation coverages, and environmental disturbances. The proposed method shows great promise in IML spatiotemporal interpretation and provides an alternative tool for the validation of large-scale IML products from space-borne or airborne remote sensing platforms.