Optimized Deep Network for Precise Digital Terrain Model Extraction from Light Detection and Ranging Data

Classifying and filtering non-ground objects from the point cloud data are among the major challenges to the development of a digital terrain model (DTM). This paper proposes a hierarchical deep network to filter and classify non-ground objects from the point cloud data. The proposed network is mainly based on a deep encoder-decoder network with effective convolutional connections for extracting and fusing the features of shallow and deep layers to detect the objects better. In the proposed encoder-decoder network, a feature extraction block was designed to extract various features at different levels. The network also adopts a global-local feature fusion strategy. The proposed hierarchical deep network is based on the extraction and gradual fusion of features on different scales to extract objects of various dimensions and densities in urban areas with different topographical conditions. Evaluation of the proposed