Ortho-image Mosaic Production System with Prioritized Stacking
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Solution Overview
Problem
Current techniques for generating ortho-image mosaics fail to produce highly accurate geopositioned images with minimal pixel shear at seam lines and do not balance image intensities effectively, leading to suboptimal image quality and currency.
Innovation Solution
An end-to-end system integrates algorithms to automatically register and ortho-rectify images, prioritize them based on currency, cloud cover, and quality, and perform intensity balancing using a cloud mask and bundle adjustment to minimize shear and radiometric differences, resulting in a cloud-free and accurate ortho-mosaic image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current mosaic generation techniques are used, then processing speed is maintained, but image accuracy and seam quality deteriorate due to pixel shear and unbalanced intensities
Solution Approach 1:
The system performs preliminary actions by automatically selecting and prioritizing images based on currency, cloud cover, and quality metrics before mosaic generation. This pre-processing step ensures that the most suitable images are chosen in advance, improving geopositional accuracy without adding complexity during the actual mosaic creation process
Solution Approach 2:
The system applies parameter changes through intensity balancing adjustments and geometric corrections during mosaic generation. By dynamically adjusting image parameters such as brightness, contrast, and geometric transformation, the system minimizes pixel shear at seam lines and achieves high geopositional accuracy while maintaining processing efficiency
2Reliability
If image selection is based on currency and quality, then image quality improves, but processing time increases due to multiple selection criteria
Solution Approach 1:
The system changes parameters by establishing a prioritization framework that evaluates images based on multiple criteria (currency, cloud cover, quality) simultaneously. This allows the system to quickly rank and select the best images without requiring sequential analysis of each criterion, thereby maintaining high image quality while minimizing processing time
Solution Approach 2:
The system uses copying by creating thumbnail versions or simplified representations of images for initial screening. This allows rapid assessment of multiple images against selection criteria, with only the most promising candidates undergoing full quality analysis, thus reducing overall processing time while maintaining reliable image quality selection
3Manufacturing precision
If intensity balancing is applied, then seam visibility reduces, but processing complexity increases
Solution Approach 1:
The system applies parameter changes through automated intensity balancing that adjusts brightness, contrast, and color parameters across multiple images. By using algorithms that analyze histogram distributions and apply transformations to match intensities between overlapping regions, the system minimizes seam visibility while maintaining consistent processing complexity through standardized adjustment procedures
Data Source
AI summary
A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.