Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

Area of Science:

• Photogrammetry and Remote Sensing
• Computer Vision
• Geospatial Analysis

Background:

• Advancements in sensors and computer vision enable high-automation 3D reconstruction from Unmanned Aerial Vehicle (UAV) imagery.
• Sun reflection effects (hotspot and sun glint) degrade multispectral image quality, impacting photogrammetric and remote sensing products.
• Radiometric defects reduce image contrast, color fidelity, and the accuracy of computed agronomical parameters.

Purpose of the Study:

• To propose an automatic approach for detecting sun reflection problems in UAV-acquired multispectral images.
• To integrate this detection strategy into flight planning and control software.
• To enhance the quality of cartographic products and derived parameters by excluding defective areas.

Main Methods:

• Development of an automatic sun reflection detection algorithm based on a photogrammetric strategy.
• Integration of the algorithm into custom flight planning and control software.
• Exclusion of image areas affected by sun reflections from further processing.

Main Results:

• Successful detection and exclusion of image areas with sun reflection problems.
• Improved quality of cartographic products such as digital terrain models and orthoimages.
• Enhanced accuracy of computed agronomical parameters, like the Normalized Difference Vegetation Index (NDVI).

Conclusions:

• The developed approach effectively detects sun reflections in UAV multispectral imagery.
• Excluding sun-affected areas significantly improves the radiometric quality and utility of geospatial products.
• The method achieves high precision (around 10 pixels error at 5 cm GSD), suitable for agricultural applications using current UAV technology.