Automated Metric Information Network for Orthomosaic Alignment
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Solution Overview
Problem
The production of orthomosaic images from satellite and aerial imagery often results in misalignment errors due to the labor-intensive and computationally intensive process of manually selecting and aligning ground control points (GCPs), which can lead to noticeable errors at the edges of input scenes.
Innovation Solution
A method for creating a geodetic network by generating and updating GCPs from overlapping images using automated tie pointing and clustering algorithms, allowing for the selection of GCPs without human intervention and performing sequential fusion of block adjustments without inline image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and alignment of ground control points is performed, then alignment accuracy can be controlled, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system automatically selects and aligns ground control points without requiring manual human intervention. The automated tie-pointing process extracts features from images, matches them across multiple images, and establishes the geodetic network independently, eliminating the time-consuming manual selection process while maintaining alignment accuracy through computational algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of selecting and measuring ground control points with an automated computational system. The mechanical action of manually identifying and measuring points is substituted by computer-based image processing, feature extraction, and automated coordinate transformation algorithms that perform the same function much faster.
2Productivity
If automated tie-pointing process is used to generate GCPs, then productivity increases, but measurement precision may be compromised
Solution Approach 1:
The automated tie-pointing process incorporates feedback mechanisms where the system continuously evaluates the quality of matched features and adjusts the alignment process accordingly. The bundle adjustment algorithm iteratively refines the GCP coordinates based on the consistency of measurements across multiple images, ensuring that automated processing achieves both high productivity and maintained precision through iterative optimization.
Solution Approach 2:
The system performs preliminary processing of images to extract and pre-organize potential tie points before the main alignment computation. By pre-processing to identify and validate candidate GCPs, the system prepares high-quality measurement data in advance, which then enables rapid and accurate automated alignment without compromising precision for the sake of speed.
3Measurement precision
If serial processing is used for bundle adjustment, then computational accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The patent segments the bundle adjustment process into smaller, manageable computational blocks that can be processed in parallel. Rather than performing a single large serial adjustment across all images and points, the system divides the geodetic network into segments and processes adjustments independently in parallel, then merges the results. This maintains the accuracy of comprehensive adjustment while dramatically reducing total computational time through parallelization.
Data Source
AI summary
A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.