Color Balance Method of Dense Point Cloud in Landslides Area Based on UAV Images

Landslides are a natural geological phenomenon and often cause economic losses, property damage, and loss of lives. The detection and recognition based on the dense point cloud derived from Unmanned Aerial Vehicle (UAVs) images play an important role in understanding the mechanism of landslides. However, the brightness difference of UAV images with large altitude differences usually makes the dense point cloud within a landslide area show color stratification, and the processing results of popular commercial and open-source software are not ideal. In this study, a multi-view UAV image brightness compensation method based on regular 3D points is proposed, and is combined with the open-source software OpenMVG and OpenMVS to obtain a dense point cloud with balanced color on the landslide's surface. The selected study area is located in the active landslide region caused by the Tengqing coal mining area in Guizhou Province, China. The terrain following flight method was used to acquire complete high resolution UAV images of the study area. Experimental results show that the global gain and offset compensation methods proposed in this study can effectively reduce the 22.3% brightness difference in the overlapping area of the adjacent images. The comparison results and the processing results of other commercial software show that the method can be applied to the color balance of the dense point cloud for UAV images with large-altitude differences.

Automated summary

This study proposes a multi-view UAV image brightness compensation method and combines it with open-source software to obtain a dense point cloud with balanced color. Experimental results show that the method effectively reduces the brightness difference in the overlapping area of adjacent images.