Urban building change detection method based on LiDAR point cloud spatial difference analysis

Abstract

The invention discloses an urban building change detection method based on LiDAR point cloud spatial difference analysis. The method comprises the steps that 1, LiDAR point cloud data with different time phases are respectively preprocessed, and DSM and DTM of the different time phases are obtained through a linear interpolation algorithm; 2, two periods of the DSM obtained in the step 1 are subtracted to obtain an elevation difference model; 3, spatial difference analysis is carried out on the elevation difference model, and a candidate change area is extracted; 4, spatial features of a LiDAR point cloud are used for extracting direct features and elevation grain features; 5, building and vegetation classification is achieved; 6, superposition and overlay analysis is carried out on the candidate change area, and the building change attribute is determined. The advantages of the LiDAR data on three-dimensional representation of the building complex structure and the topological relation are fully achieved, and building three-dimensional change detection is carried out.

Description

Technical field [0001] The present invention relates to the technical field of airborne lidar, in particular to a method for detecting changes in urban buildings based on LiDAR point cloud spatial difference analysis. Background technique [0002] With the continuous development of economic construction, my country's urban construction is in full swing, and urban changes are very frequent and intense. Remote sensing (including aerial) images have high geometric accuracy and rich semantic information. They are currently the largest data source for large-scale and high-frequency urban change detection. In recent decades, many scholars have conducted in-depth research on change detection based on remote sensing images, put forward many representative algorithms, and formed a rich theoretical system. However, the current remote sensing image-based change detection still faces many problems such as occlusion and shadow, poor projection, internal heterogeneity of features, excessive t...

AI Technical Summary

Problems solved by technology

At present, there are still some problems that need to be solved in LiDAR data, such as the lack of data caused by the occlusion that is not penetrated by the laser beam, the sparse data points, regional holes or data noise due to flight height, scanning angle and reflectivity of ground objects. Feature extraction and classification problems based on discrete point clouds, etc.

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