Satellite Image Geolocation Correction Using Mobile Road Trajectories
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Solution Overview
Problem
Satellite imagery geolocation accuracy is compromised by deviations between actual and reported satellite ephemeris and sensor pointing direction, leading to coordinate inaccuracies due to slight misalignments in pitch and roll.
Innovation Solution
A method involving mobile mapping resources to identify pose points on roadways, applying a sensor model with adjustable pitch and roll parameters, and using a weighting function to optimize image uniformity, thereby correcting satellite image geolocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite ephemeris and sensor pointing data are used directly from metadata, then processing is simple and fast, but geolocation accuracy deteriorates due to offset errors
Solution Approach 1:
The patent introduces an intermediary optimization process that uses mobile mapping data as a mediator between satellite metadata and final geolocation. The method projects mobile mapping pose points onto the satellite image, calculates offset errors, and uses these offsets to correct the geolocation without requiring complex real-time adjustments to satellite ephemeris or sensor pointing data
Solution Approach 2:
The patent changes the parameters used for geolocation by introducing offset corrections derived from mobile mapping data. Instead of relying solely on satellite metadata parameters (ephemeris, attitude, pointing), the system adjusts these parameters by adding calculated offsets that compensate for systematic errors, thereby improving accuracy while maintaining relatively simple processing
2Measurement precision
If sensor model parameters are adjusted to correct pointing errors, then geolocation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial correction by focusing only on the most significant error sources. Instead of adjusting all sensor model parameters comprehensively, the method calculates and applies offsets specifically for pitch and roll pointing errors using mobile mapping pose points, achieving sufficient accuracy improvement without exhaustive computational effort
Solution Approach 2:
The system uses the satellite image itself and accompanying mobile mapping data to self-correct its geolocation errors. By projecting pose points onto the image and calculating offsets from the mismatch between expected and actual positions, the system performs self-calibration without requiring external reference data or complex iterative optimization
Data Source
AI summary
Satellite images have inherent geo-positional errors of orders a few meters. Corrections are achieved by adjusting a sensor model which maps ground coordinates of control features into image coordinates and establishing a correspondence between the ground and image features, in this case a road network. The ground coordinates are obtained from mobile pose points. To adjust the sensor model we rely on the fact that the roads are typically much more uniform than surrounding features, and therefore have smaller entropy. The sensor model is adjusted so that the image pixels, obtained from projecting ground coordinates of the mobile pose points onto the image, minimize the entropy of the pixels that represent the road network.