Airborne Image Aggregation via Statistical Resampling
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Solution Overview
Problem
Current airborne imaging systems for agricultural crop monitoring are inefficient and costly due to computationally intensive mosaicking algorithms, which struggle with repeating spatial patterns and require high overlap and processing resources, leading to ineffective and time-consuming data aggregation.
Innovation Solution
A system and method utilizing hardware processors to obtain and analyze high-resolution image data from airborne vehicles, resampling statistical information at a lower spatial frequency for efficient aggregation and display, suitable for disadvantaged regions with limited computing resources, enabling immediate and cost-effective crop monitoring metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional mosaicking algorithms are used to aggregate airborne images, then spatial coverage can be achieved, but computational intensity increases exponentially and processing time becomes excessively long
Solution Approach 1:
The patent divides the image aggregation task into two distinct stages: (1) extracting statistical features (mean, standard deviation) from individual images, and (2) aggregating these statistical features across images. This segmentation transforms the computationally intensive pixel-by-pixel mosaicking process into a much faster statistical aggregation process, dramatically reducing processing time while maintaining spatial coverage.
Solution Approach 2:
The patent extracts meaningful statistical information (mean and standard deviation values) from the image data and separates this extraction step from the aggregation step. By taking out the essential statistical features and aggregating only these compact representations rather than entire images, the system achieves fast processing without sacrificing the completeness of spatial coverage.
2Measurement precision
If traditional mosaicking algorithms process high-resolution image data, then detail accuracy is maintained, but processing resources and computational cost become excessively high
Solution Approach 1:
The patent extracts statistical summaries (mean and standard deviation) from the high-resolution image data, capturing the essential information in a compressed format. This extraction allows the system to maintain measurement precision through the statistical representations while dramatically reducing the amount of data that needs to be processed, thereby lowering processing resource requirements.
Solution Approach 2:
The patent changes the representation parameters from raw pixel data to statistical parameters (mean and standard deviation). This parameter transformation preserves the essential information about image content while reducing the data dimensionality and complexity, enabling processing with significantly fewer computational resources.
3Area of stationary object
If traditional mosaicking methods are applied to agricultural crops with repeating patterns, then spatial coverage is achieved, but algorithm effectiveness decreases due to lack of unique features
Solution Approach 1:
The patent extracts statistical features (mean and standard deviation) from each image independently, which preserves information about repeating patterns and spatial variations. By aggregating these statistical features rather than relying on feature matching, the algorithm remains effective for agricultural crops with repeating patterns, maintaining both spatial coverage and reliability.
4Measurement precision
If high overlap is required for mosaicking to ensure solution quality, then measurement accuracy improves, but flight time and data volume increase significantly
Solution Approach 1:
The patent extracts statistical summaries from images, which captures the essential spatial information in a compressed form. This allows the system to achieve measurement accuracy with significantly less data overlap, as the statistical features can be aggregated effectively even with minimal overlap between images, thereby reducing the required data volume and flight time.
Data Source
AI summary
Spatially variable data associated with a geographical region such as a map or image from multiple samples acquired by one or more airborne vehicles taken across sub-regions of the geographical region may be aggregating and displayed. High-resolution image data of a geographical region acquired by one or more airborne vehicles may be obtained. The image data may comprise images corresponding to sub-regions of the geographical region. The images may be acquired at an image resolution corresponding to a first spatial frequency. Individual images may be analyzed to determine statistical information corresponding to the sub-regions of the geographical region. The statistical information corresponding to the sub-regions of the geographical region may be provided, for presentation to a user, by resampling the statistical information based on a second spatial frequency. The second spatial frequency may be equal to or less than the first spatial frequency.