Satellite Imaging System Self-Calibration Without Ground Control Points
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Solution Overview
Problem
Satellite image correction methods require ground control points, which are not always available, and internal/external parameter changes over time can affect image accuracy, especially due to weight, space, and power constraints on satellites.
Innovation Solution
A method that provides at least three images of an area from different angles, establishes point correspondence, generates two sets of three-dimensional information, compares them for discrepancies, and uses these discrepancies to update internal and external parameters of the imaging system, allowing for image correction without ground control points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground control points are used for image correction, then image accuracy is improved, but the method becomes inapplicable when ground control points are not available
Solution Approach 1:
The system performs self-calibration by using multiple images of the same area taken from different angles. The imaging system corrects its own parameters by comparing three-dimensional information derived from different image combinations, eliminating the need for external ground control points.
Solution Approach 2:
The system establishes feedback loops by comparing three-dimensional information from different image combinations and using the discrepancies to update imaging system parameters. This continuous feedback mechanism allows the system to self-correct and improve accuracy over time without external references.
2Measurement precision
If extra equipment is added to satellites for monitoring sensor changes, then measurement precision is improved, but weight, space, and power constraints are violated
Solution Approach 1:
The imaging system monitors and corrects its own parameter changes using only the images it captures. By analyzing discrepancies in three-dimensional information from different image combinations, the system detects sensor drift and corrects it without requiring separate monitoring equipment.
Solution Approach 2:
The same imaging system that captures images for mapping purposes is also used for monitoring its own parameter changes. The multi-functional approach allows the system to serve both imaging and self-diagnosis functions, eliminating the need for dedicated monitoring equipment.
3Reliability
If extra equipment is added to satellites for monitoring sensor changes, then reliability is improved, but weight, space, and power constraints are violated
Solution Approach 1:
The system maintains reliability by performing self-diagnosis and self-correction using its existing imaging capabilities. The system detects parameter drift through image analysis and automatically corrects it, maintaining reliable operation without additional monitoring equipment.
4Measurement precision
If multiple images from different angles are processed to generate three-dimensional information, then image correction accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The processing is divided into distinct segments: first establishing point correspondence across multiple images, then generating three-dimensional information from different image combinations, and finally comparing results to determine corrections. This segmentation allows for systematic and efficient processing.
Solution Approach 2:
The system performs preliminary processing by establishing point correspondence between images before generating three-dimensional information. This preliminary step organizes the data in a way that facilitates efficient subsequent processing and comparison.
Data Source
AI summary
A method is provided, which comprises generating at least three images of an area of interest from at least one imaging system, the generated images being provided from at least three different angles, establishing point correspondence between the provided images. The method further involves generating at least two sets of three-dimensional information based on the provided images, wherein the at least two sets of three-dimensional information are generated based on at least two different combinations of at least two of the at least three provided images of the area of interest. The method further includes comparing the at least two sets of three-dimensional information so as to determine discrepancies, and providing information related to the imaging system or errors in the images based on the determined discrepancies.