Coverage Mask Generation for Large Satellite Imagery
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Solution Overview
Problem
The updating of large-scale satellite imagery and terrain data for interactive 3D map systems is a time-consuming and laborious process, requiring efficient methods to identify and process valid and non-valid data to reduce processing time and costs.
Innovation Solution
A computer-implemented method and system that generates a coarse resolution image and a coverage mask to identify valid and non-valid data, allowing for parallel processing and accurate identification of no-data values and compression artifacts, while preventing shadows and valid data from being misclassified as non-valid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential processing methods are used to generate coverage masks for large satellite imagery, then processing accuracy can be maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides large satellite imagery into multiple tiles and processes them in parallel. Coverage masks are generated for individual tiles separately, then combined to form the complete coverage mask. This segmentation enables distributed computing across multiple processors or machines, dramatically reducing processing time while maintaining the accuracy of coverage mask generation through consistent algorithms applied to each tile.
2Reliability
If flood-filling is applied to all pixels without threshold filtering, then all potential no-data areas are identified, but valid data such as shadows are misclassified as non-valid
Solution Approach 1:
The patent introduces a hole size threshold parameter that filters flood-filled regions based on their area. Small regions that might represent valid features like shadows are excluded from being classified as no-data, while larger regions are correctly identified as no-data areas. This parameter-based filtering resolves the contradiction by adjusting the classification criteria to distinguish between valid and invalid data based on spatial extent.
3Measurement precision
If high-resolution imagery is processed directly without downsampling, then detailed accuracy is maintained, but computational complexity and processing time increase
Solution Approach 1:
The patent segments high-resolution imagery into smaller tiles, which reduces the computational complexity of processing each individual tile. The segmentation approach allows parallel processing of multiple tiles, maintaining overall image detail accuracy while distributing the computational load across multiple processing units, thus resolving the contradiction between detail preservation and computational complexity.
4Productivity
If parallel processing of image tiles is implemented, then processing time is reduced, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the image processing task into independent tile operations that can be executed in parallel. Each tile is processed independently to generate its coverage mask, eliminating the need for complex inter-tile coordination during the processing phase. The segmentation strategy simplifies the parallel processing architecture by creating self-contained processing units that only require simple aggregation of results.
Solution Approach 2:
The patent applies the same coverage mask generation algorithm uniformly across all image tiles. By copying and executing the identical processing logic for each tile, the system avoids the complexity of implementing different processing strategies for different regions. This uniform approach simplifies system coordination while maintaining processing throughput through parallel execution.
Data Source
AI summary
A computer-implemented method is described. The method includes generating a coarse resolution image from a finer resolution image, generating a coarse coverage mask that identifies valid and non-valid data in the coarse resolution image, and determining whether data in the finer resolution image is valid or non-valid data based on whether corresponding data identified by the coarse coverage mask is valid or non-valid data. In certain embodiments, generating a coarse resolution image can include segmenting the finer resolution image into tiles and compressing each tile. Compressing each tile can be preformed using a lossless compression, and at least two tiles can be transmitted to separate computing devices and compressed in parallel.