Panoramic Imagery Geo-rectification Automation
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Solution Overview
Problem
Existing post-processing systems for aerial imagery lack automation in correcting camera position and metadata errors, requiring manual intervention for geo-rectification and image registration.
Innovation Solution
A post-processing panoramic imagery geo-rectification system that uses a multi-step process to determine camera position errors and a two-step process for image registration, employing machine-learning models, Fast Fourier Transform, and Perspective-n-Point algorithms to automate the correction of metadata and stitching errors without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or human interaction is used to correct camera position and add warp-defining metadata, then accuracy in geo-rectification can be achieved, but productivity is reduced due to time-consuming manual processes
Solution Approach 1:
The system enables self-service automation by using machine learning models to automatically detect geometric primitives, calculate camera positions, and generate warp-defining metadata without human intervention. The automated pipeline processes imagery through multiple subprocesses including horizon detection, vanishing point calculation, and perspective-n-point algorithms to self-correct geo-rectification errors.
Solution Approach 2:
The patent replaces manual mechanical processes with computational algorithms. Machine learning models substitute human visual inspection and manual measurement with automated image processing techniques including convolutional neural networks for primitive detection and mathematical algorithms for camera pose estimation, thereby increasing productivity while maintaining accuracy.
2Productivity
If automation is implemented to increase productivity in post-processing, then processing speed improves, but measurement precision may deteriorate due to algorithmic errors
Solution Approach 1:
The system incorporates feedback mechanisms through iterative optimization algorithms that refine camera position estimates. The perspective-n-point algorithm uses feedback from detected geometric primitives to iteratively improve camera pose accuracy. Additionally, the system validates automated results against known geometric constraints to ensure precision is maintained.
Solution Approach 2:
The patent applies preliminary action by using machine learning models to pre-detect geometric primitives such as horizons, building edges, and road intersections before performing camera position calculation. This preliminary detection phase prepares accurate input data for subsequent algorithms, ensuring that automation maintains high measurement precision from the outset.
3Measurement precision
If complex multi-step processes are used to solve six degrees of freedom for camera position, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex geo-rectification process into distinct subprocesses: (1) detection of geometric primitives using machine learning, (2) calculation of vanishing points and horizons, (3) estimation of camera pose parameters, and (4) generation of warp-defining metadata. Each segment handles a specific aspect of the problem, making the overall complex system more manageable and implementable while maintaining high precision through specialized algorithms in each segment.
Data Source
AI summary
A system for performing a post-processing panoramic imagery geo-rectification process is provided. The system is for use with aerial imagery and is agnostic to where the system operates, which may be a computing device including, without limitation, a PC, servers, or in the cloud. This process includes at least two subprocesses, wherein subprocess 1 includes instructions to solve six degrees of freedom for camera position, without human intervention; namely the latitude, longitude, altitude, pan, tilt, and roll. Subprocess 2 includes a method for placing warp points, which define a thin-plate spline that can be utilized on a server or within a compatible software system.