항공 라이다 점군 분류를 위한 기하학적 특성정보 기반 딥러닝

다국어 초록 (Multilingual Abstract)

The use of photogrammetry is rapidly increasing in many fields due to the development of various types of advanced sensors that can be mounted on a variety of payloads, along with state-of-the-art data processing technology, including DL (Deep Learning), which can efficiently produce user-oriented spatial information products. The performance of DL is affected by various factors, including neural network architecture, training data and method. This paper presents multimodal DL for LiDAR (Light Detection and Ranging) point cloud classification by utilizing geometric features derived from the 3D coordinates of LiDAR data. In particular, omnivariance, eigenentropy, anisotropy, surface variation, sphericity, and verticality, as geometric features computed from the eigenvalues of the point clouds, were utilized for training the DL model. Each feature represents unique intrinsic information about the objects. By revealing these characteristics inherent in the 3D coordinates of LiDAR data, a synergy effect in DL model training can be achieved to improve DL performance. Additionally, fusion is an important issue in multimodal DL. In this paper, we analyzed the classification from early-fusion and hybrid method based on late-fusion. The overall accuracy of the classification improved by up to 35% for test data by utilizing geometric features with early-fusion. Therefore, multimodal DL could be an effective training strategy by utilizing intrinsic feature information.