Point cloud level fusion method of laser radar data and hyperspectral image

Abstract

The invention discloses a point cloud level fusion method of laser radar data and a hyperspectral image. The method mainly aims at heterogeneous remote sensing data, namely the laser radar data and hyperspectral image data, and is characterized in that firstly unmixing is conducted on the hyperspectral data on the basis of a non-negative matrix decomposition framework added with regular terms to obtain a hyperspectral image data abundance matrix and an end member matrix; according to abundance information of a final abundance matrix of the hyperspectral image data, an abundance matrix of the laser radar data is obtained through a bilinear interpolation method; and finally the abundance matrix of the laser radar data is fused with a final hyperspectral end member matrix to obtain a hyperspectral point cloud. According to the method, the hyperspectral high-resolution spectral information and the small-spot laser radar elevation information are fused at the point cloud level, and the capabilities of carrying out spectral classification and 3D structure segmentation are achieved at the same time.

Description

technical field [0001] This application relates to the technical field of multi-source remote sensing data fusion, in particular to a point cloud-level fusion method of lidar data and hyperspectral images. Background technique [0002] The intelligentization of remote sensing earth observation and the demand for multi-directional and three-dimensional detection make the fusion of multi-source remote sensing data an unprecedented urgent demand. Especially in recent years, the fusion of elevation information from lidar scans and hyperspectral image data has provided a potential solution for such application requirements. [0003] Multi-source data fusion is usually divided into pixel-level fusion, feature-level fusion, and decision-level fusion. Pixel-level fusion is often used for image data with different spectral characteristics, such as the fusion of multispectral and panchromatic images, hyperspectral and multispectral images. However, lidar data and hyperspectral image...

AI Technical Summary

Problems solved by technology

In fact, massive point cloud data contains rich spatial geometric structure information, and related research on feature-level fusion has failed to fully mine the information in point clouds.

In addition, image fusion based on low spatial resolution will lead to loss of effective information

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