Radiometric Terrain Correction via Cloud Segmentation
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Solution Overview
Problem
Performing radiometric terrain correction is challenging due to the complexity of observational data sets and the computational resources required, necessitating efficient systems and techniques for data processing and analysis.
Innovation Solution
The implementation of a cloud-based platform that processes and analyzes large datasets, such as those from Landsat and MODIS satellites, using commodity cloud computing resources, which enables efficient radiometric terrain correction by converting raw data into a calibrated, georeferenced, and multi-resolution tiled format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiometric terrain correction is performed using traditional computational methods, then correction accuracy is maintained, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent segments the terrain correction process into distinct computational stages: radiometric calibration, terrain correction, and atmospheric correction. Each stage processes specific parameters independently, allowing parallel computation and reducing overall computational resource consumption while maintaining correction accuracy.
Solution Approach 2:
The patent performs preliminary radiometric calibration and terrain correction on satellite imagery before detailed analysis. By pre-processing the data to correct for terrain effects and radiometric variations, the system reduces the computational burden on subsequent processing stages while ensuring accurate correction results.
2Manufacturing precision
If radiometric terrain correction is performed on large observational datasets, then correction completeness is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent divides large observational datasets into manageable processing units or tiles, allowing parallel processing across multiple computational cores or distributed systems. This segmentation enables complete correction of all data while reducing overall processing time through concurrent execution.
Solution Approach 2:
The patent implements iterative processing where critical regions or time periods are processed first with full correction, while less critical areas use optimized or simplified correction methods. This approach ensures correction completeness for important data while reducing processing time for the overall dataset.
Data Source
AI summary
Performing radiometric terrain correction includes receiving an uncorrected image and acquisition geometry metadata. It further includes determining an acquisition classification based at least in part on the acquisition geometry metadata. It further includes based at least in part on the acquisition classification, determining whether a correction factor corresponding to the acquisition classification exists. It further includes in response to determining that the correction factor corresponding to the acquisition classification does not exist: determining a correction factor; and storing the correction factor. It further includes associating the uncorrected image with the correction factor.