3D Terrain Matching via Cross-Correlation of Altitude Models
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Solution Overview
Problem
Existing methods for matching 3D terrain information from aerial images captured at different altitudes require Ground Control Points (GCPs), which are time-consuming and expensive to measure, especially for large areas, and lack an automated quantified optimization method for precise alignment.
Innovation Solution
A method and apparatus that convert high-altitude and low-altitude 3D terrain information into numerical models, calculate matching parameters through cross-correlation, and adjust geospatial coordinates to align the data without GCPs, using hierarchical pyramid data and Euclidean transforms for precise matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Ground Control Points (GCPs) are used to match 3D terrain information from different altitudes, then matching precision is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent extracts and removes the dependency on Ground Control Points (GCPs) from the terrain matching process. By using feature point extraction and matching algorithms directly on the aerial images and 3D terrain data, the method eliminates the need for separate GCP measurement activities, thereby reducing time consumption while maintaining matching precision through automated feature-based registration
Solution Approach 2:
The patent replaces the manual mechanical process of GCP measurement and field surveying with an automated computational system. Image processing algorithms, feature detection methods, and automated coordinate transformation algorithms substitute for manual field work, significantly reducing time consumption while achieving precise matching through computational optimization
2Measurement precision
If Ground Control Points (GCPs) are measured for large area terrain matching, then matching accuracy is improved, but expense increases significantly
Solution Approach 1:
The patent enables the terrain matching system to be self-sufficient by automatically extracting features from the aerial images and 3D terrain data themselves. The system uses inherent features in the data (buildings, terrain structures, road networks) as matching references, eliminating the need for external GCP infrastructure and associated costs while maintaining high matching accuracy through robust feature-based registration
3Manufacturing precision
If low-altitude aerial images are used for large area 3D reconstruction, then detailed terrain information is improved, but time and expense for image capture and reconstruction increase
Solution Approach 1:
The patent segments the large area into multiple overlapping sub-areas, each reconstructed using high-detail low-altitude aerial images. By dividing the overall reconstruction task into manageable segments that can be processed independently and then integrated through the automated matching system, the method maintains high detail quality while improving overall productivity through parallel processing and reduced computational burden per segment
Solution Approach 2:
The patent transitions from attempting to reconstruct entire large areas at once to processing data in hierarchical levels - first matching and integrating multiple low-altitude image sets at local levels, then progressively integrating these into a comprehensive high-altitude view. This multi-dimensional approach allows detailed reconstruction to be achieved efficiently by working through multiple scales and resolutions
Data Source
AI summary
Disclosed herein are a method and apparatus for matching 3D terrain information based on aerial images captured at different altitudes. The method includes receiving a high-altitude numerical height model based on a terrain image captured at a specific high altitude; receiving 3D terrain information observed from a low altitude, which is generated based on a terrain image captured at an altitude lower than the specific high altitude; generating a low-altitude numerical height model by converting the 3D terrain information into a numerical model in the same form as the high-altitude numerical height model; measuring the cross-correlation between the high-altitude numerical height model and the low-altitude numerical height model, thereby calculating matching parameters for enabling the low-altitude numerical height model to match the high-altitude numerical height model; and adjusting the geospatial coordinates of the 3D terrain information based on the matching parameters and outputting georeferenced 3D terrain information in the same coordinate system as the high-altitude numerical height model.