Point cloud classification method based on multi-level aggregation feature extraction and fusion

Abstract

The invention provides a point cloud classification method based on multi-level aggregation feature extraction and fusion. The method comprises the following steps: (1) constructing a multi-level point set; (2) point set feature extraction based on LLC-LDA; (3) point set feature extraction based on multi-scale maximum pooling (LLC-MP); and (4) point cloud classification based on multi-level pointset feature fusion. The invention provides a multi-level point set aggregation feature extraction and fusion method based on multi-scale maximum pooling and local Discriminant alteration (LDA), and point cloud classification is realized based on the fused aggregation features. The multi-level point set aggregation feature extraction and fusion method based on multi-scale maximum pooling is used for point cloud classification. According to the algorithm, multi-level clustering is carried out; adaptively acquiring a multi-level and multi-scale target point set; the method comprises the followingsteps of: expressing point cloud single-point features by using local linear constraint sparse coding (LLC), and extracting the point cloud single-point features; a scale pyramid is constructed by using point coordinates, features capable of representing local distribution of a point set are constructed based on a maximum pooling method, then the features and an LLC-LDA model are fused to extractglobal features of the point set, and finally point cloud classification is realized by using multi-level aggregation features of the fused point set.

Description

technical field [0001] The invention relates to a point cloud classification method based on multi-level aggregation feature extraction and fusion, and belongs to the field of intelligent processing of laser radar remote sensing data. Background technique [0002] In recent years, lidar sensors have been widely used in many fields. Processing laser point cloud data is an important step in applications in areas such as autonomous driving, smart cities, and surveying and mapping remote sensing. Semantic segmentation of point cloud is an important basis for point cloud data processing and wide application of data. Since the point cloud contains a variety of complex ground objects with different sizes and geometric structures, it becomes very challenging to accurately and effectively classify each point. Therefore, research on point cloud semantic segmentation is of great significance. [0003] At present, researchers have proposed a large number of point cloud semantic segme...

AI Technical Summary

Problems solved by technology

In the above methods, only the point set features constructed based on the LDA model are usually used, and the global features of the point set represented by the LDA point set feature lack features such as local structure in the point set.

Images