Airborne laser point cloud classification method based on local and global depth feature embedding

• Main Authors: HUANG RONG, GU ZHENXIONG, XU YUSHENG, PAN YUE, HONG DANFENG
• Format: Patent
• Language: Chinese; English
• Published: 22.10.2019
• Subjects: CALCULATING; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention relates to an airborne laser point cloud classification method based on local and global depth feature embedding. The method comprises the following steps: 1, preprocessing urban scene point cloud data, inputting Point Net++, and obtaining an initial soft label and depth features; 2, embedding the depth features and spatial information in the urban scene point cloud data into an optimization domain, and representing the optimization domain by using a local spatial manifold learning method; and 3, performing classification result optimization on the features based on local data and global feature correlation optimization, which are obtained based on the initial soft label in combination with the optimization domain and are expressed through dimension reduction, by using globalspace regularization to obtain a final point cloud classification result. Compared with the prior art, the method not only optimizes feature learning, but also solves the problem of local and globalmark smoothing, and has th