Obscured Antenna Dish Contour Reconstruction Using LSF Outlier Filtering
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Solution Overview
Problem
Existing image reconstruction methods struggle to accurately reconstruct partially obscured elliptical antenna dishes and parallelogram solar panels due to occlusion by other objects in satellite, stationary building, or marine vessel images.
Innovation Solution
A computer-implemented method using least squares fit (LSF) and centroid-based analysis to identify and exclude outlier samples, and a flip-and-combine process for reconstructing obscured contours of antenna dishes and solar panels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image reconstruction methods are used on partially obscured objects, then the processing is simple, but the reconstruction accuracy is poor
Solution Approach 1:
The patent segments the contour into multiple samples and divides them into inlier sets and outlier sets based on LSF residual analysis. This segmentation allows the method to selectively process reliable contour portions while identifying and excluding unreliable portions caused by occlusion, thereby improving reconstruction accuracy without requiring complex global processing of the entire obscured contour.
Solution Approach 2:
The patent extracts and removes outlier samples from the contour data that are identified as being affected by occlusion. By taking out these problematic samples through statistical analysis of LSF residuals and ensemble mean deviations, the method prevents them from degrading the overall reconstruction quality, achieving high accuracy without complex occlusion handling mechanisms.
2Measurement precision
If multiple sets of random samples are processed with LSF to ensure accuracy, then the reconstruction quality improves, but the computational time increases
Solution Approach 1:
The patent performs preliminary action by generating multiple sets of random samples and computing their LSF cost functions and ensemble means before final reconstruction. This preliminary statistical analysis identifies outlier regions in advance, allowing the final reconstruction to focus only on inlier samples, thereby reducing the effective computational workload while maintaining high accuracy through pre-computed statistical measures.
Solution Approach 2:
The patent changes parameters by using ensemble mean of cost functions from multiple random sample sets as a criterion for identifying outliers. By transforming individual cost function evaluations into a statistical ensemble measure, the method achieves robust accuracy assessment that filters out occlusion-affected regions, enabling accurate reconstruction with optimized computational effort through parameter-based filtering.
3Measurement precision
If outlier samples are identified and excluded using ensemble mean analysis, then the reconstruction accuracy improves, but the algorithm complexity increases
Solution Approach 1:
The patent implements feedback by using the ensemble mean of cost functions from multiple random sample sets to identify outlier samples. This feedback mechanism continuously refines the selection of inlier samples by comparing individual sample deviations against the ensemble statistic, creating a self-correcting process that improves reconstruction accuracy through iterative statistical validation without requiring complex external control systems.
Solution Approach 2:
The patent applies self-service by allowing the data itself to identify its own outliers through the ensemble mean analysis. The contour samples automatically reveal their own reliability status through statistical comparison of their LSF residuals against the ensemble distribution, eliminating the need for complex external outlier detection algorithms and enabling accurate self-filtering of occlusion-affected regions.
Data Source
AI summary
Reconstructing a partially obscured elliptical antenna dish image is provided. An image of an antenna dish contour with an obscured section is received, and a centroid for use as an origin reference is determined. Indices are sequentially assigned to samples along the contour starting with a sample having zero polar angle according to the origin reference. Sets of randomly selected N samples along the contour are prepared and used to perform least squares fit (LSF) to determine a cost function for each set. Cost functions with LSF residuals below a specified threshold show good quality-of-fit. An ensemble mean of the cost functions with good quality-of-fit is calculated. Outlier samples are identified according to local extrema for the ensemble mean. Samples having poor quality-of-fit of the ensemble mean between the extrema are considered outliers. Inlier samples are used to reconstruct the obscured section of the contour.