Drone Hyperspectral Geometric Correction via Collinearity Equation
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Solution Overview
Problem
Drone-mounted imaging hyperspectrometers face significant geometric distortion due to low stability and airflow speed, limited load capacity, and the inability to mount high-precision POS systems, making precise geometric correction of hyperspectral images challenging.
Innovation Solution
A method and system for drone-mounted imaging hyperspectral geometric correction, which involves collecting low-precision POS data in real-time, parsing precise photography center position and attitude information, and establishing a collinearity equation using a digital camera and imaging hyperspectrometer synchronized at adjacent positions, to achieve high-precision linear array position and attitude information for geometric correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional large high-precision POS system is mounted on a drone, then high-precision sensor position and attitude information can be collected, but the drone's load capacity is exceeded
Solution Approach 1:
The solution segments the measurement task into two parts: a lightweight POS sensor mounted on the drone for real-time position and attitude collection, and a ground-based station for establishing high-precision coordinate reference. This segmentation allows the drone to carry only minimal sensing equipment while achieving high-precision measurements through the combined system.
Solution Approach 2:
A ground-based reference station serves as an intermediary between the drone's lightweight POS system and the final high-precision coordinate system. The reference station provides reference coordinates that enable the conversion of relatively precise drone measurements into high-precision geographic coordinates without requiring the drone to carry heavy equipment.
2Productivity
If a linear array sensor push-broom imaging mode is used on a drone, then imaging hyperspectral data can be collected, but obvious geometric distortion occurs due to linear array position and attitude changes
Solution Approach 1:
The system implements feedback by continuously collecting real-time position and attitude information from the POS sensor during the push-broom imaging process. This feedback data is then used to calculate and correct the geometric distortion of each spectral line, allowing the system to maintain high imaging efficiency while achieving accurate geometric correction through continuous measurement and adjustment.
Solution Approach 2:
The system performs preliminary measurement by collecting position and attitude data throughout the imaging process, which is then used in subsequent correction calculations. By having this measurement data prepared in advance, the system can efficiently correct geometric distortion without requiring re-imaging or manual measurement after the fact.
3Device complexity
If low-precision POS data is used for geometric correction, then the correction process can be simplified, but geometric distortion of hyperspectral images cannot be effectively restored
Solution Approach 1:
A ground-based reference station acts as an intermediary that provides high-precision reference coordinates. This reference station enables the system to achieve high geometric correction precision by providing accurate reference points for coordinate transformation, while the correction system itself remains relatively simple in structure.
Solution Approach 2:
The system changes the parameter of position-attitude data precision through the combination of lightweight on-drone sensing and ground-based reference measurement. By integrating data from both sources, the system transforms relatively precise raw measurements into high-precision corrected coordinates through mathematical transformation and reference alignment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables high-precision geometric correction of hyperspectral images, overcoming the limitations of drone stability and load capacity, thereby supporting the wider application of drone-based hyperspectral remote sensing.
Implementation Method 1
A DEM (Digital Elevation Model) is a virtual representation of typography, and can be obtained by means of photogrammetry based on aerial or satellite images
Data Source
AI summary
Related are a drone-mounted imaging hyperspectral geometric correction method and a system, comprising: collecting position attitude information of a current drone low-precision POS sensor in real time; based on the position attitude information, parsing precise photography center position attitude information of a digital photograph, and generating a DEM of an area covered by the photograph; based on the precise photography center position attitude information, performing correction on position attitude data corresponding to multiple imaging hyperspectral scan lines between photography centers of adjacent digital photographs, and obtaining high-precision linear array position attitude information of the multiple imaging hyperspectral scan lines; based on the high-precision linear array position attitude information and the DEM, establishing a collinearity equation and generating a hyperspectral image.