Semi-supervised classification method for airborne laser radar data fusing images

Abstract

The invention relates to the technical field of airborne laser radar data processing, in particular to a semi-supervised classification method for airborne laser radar data fusing images. In the method, based on a semi-supervision concept, rough classification results of point cloud data are utilized to extract high-accuracy training sample data which is used for classifying high-resolution images; and in the post-processing process, based on target complexity, spurious building points are removed, the classification results are refined, and LiDAR (Laser Intensity Direction and Ranging) pointcloud multiple characteristics are fused for cross validation so as to finally realize fine classification of airborne laser radar data. The method is a fusion classification method with high reliability and high classification accuracy. In the precondition without using near infrared data, the method achieves good effect of classification for point cloud tall vegetation and low vegetation areas.

Description

Technical field [0001] The invention relates to the technical field of airborne lidar data processing, in particular to an airborne lidar data semi-supervised classification method of fusion images. Background technique [0002] Airborne LiDAR is a new type of active aerial remote sensing earth observation technology, which can directly obtain the spatial 3D point cloud information of the target. With the acceleration of urbanization, the use of airborne LiDAR technology to achieve high-precision and efficient extraction of urban terrain information is of great significance. The most basic and key technology is the classification of LiDAR point cloud data. The points classified as bare ground are used for the generation of digital ground models to provide basic data for topographic mapping, engineering surveys, environmental planning, etc.; the points classified as buildings and vegetation can be used to improve the accuracy of DTM models, and in 3D digital cities. Building mode...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the limitations of the above-mentioned classification of single-source remote sensing data, especially the lack of classification accuracy of single laser radar data, the purpose of the present invention is to provide a semi-supervised classification method for airborne laser radar data that fuses images, using semi-supervised method to train Samples, using high-resolution images and airborne LiDAR data fusion classification, and finally achieve the purpose of high-precision classification of point cloud data

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