Adaptive Downsampling Kernel for Irregular Grids
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Solution Overview
Problem
Downsampling signals from one grid to another, especially to an irregular grid, poses challenges such as susceptibility to aliasing and varying spatial resolution across the field-of-view, particularly when mapping uniform grids on the Earth's surface to nonuniform grids in an instrument's coordinate system.
Innovation Solution
An adaptive downsampling method using an automatically adjustable kernel, which determines the Modulation Transfer Function (MTF) of the instrument, calculates a kernel for each grid location, and stores these in a lookup table to maintain a substantially constant spatial resolution across the output grid, minimizing aliasing and ensuring uniform spatial resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If downsampling is performed using a fixed kernel, then computational complexity is reduced, but spatial resolution varies throughout the field-of-view
Solution Approach 1:
The patent implements dynamic kernel adjustment where the downsampling kernel is automatically adapted for each grid location based on local spatial frequency characteristics. This allows the system to maintain consistent spatial resolution across the field-of-view while managing computational complexity through selective adaptation rather than universal complexity.
Solution Approach 2:
The patent applies different downsampling kernels at different locations in the field-of-view based on local spatial frequency content. By analyzing local characteristics and applying location-specific kernels, the system achieves uniform spatial resolution without requiring maximum computational resources across the entire image.
2Quantity of substance
If downsampling is performed to reduce data size, then data rate is reduced, but aliasing increases
Solution Approach 1:
The patent performs preliminary analysis of local spatial frequencies before downsampling and pre-determines appropriate kernels for each location. This preliminary action allows the system to suppress aliasing frequencies before the actual downsampling occurs, ensuring that data reduction does not lead to increased aliasing artifacts.
Solution Approach 2:
The patent dynamically changes kernel parameters based on local spatial frequency analysis. By adjusting kernel characteristics to match local frequency content, the system maintains appropriate frequency suppression to prevent aliasing while achieving effective data size reduction through downsampling.
3Manufacturing precision
If adaptive kernel calculation is performed for each grid location, then spatial resolution consistency is improved, but computational load increases
Solution Approach 1:
The patent divides the field-of-view into discrete grid locations and calculates adaptive kernels for each location independently. This segmentation allows the system to achieve spatial resolution consistency through localized adaptation while managing computational complexity by processing locations in manageable units rather than requiring global optimization.
4Measurement precision
If mapping is performed from uniform grid to nonuniform grid, then geo-centric accuracy is improved, but spatial resolution varies across FOV
Solution Approach 1:
The patent explicitly addresses the asymmetric nature of mapping from uniform instrument grids to nonuniform geo-centric grids. By calculating location-specific adaptive kernels that account for the varying transformation characteristics at different FOV locations, the system maintains spatial resolution uniformity despite the inherent asymmetry of the coordinate transformation.
Data Source
AI summary
A method of adaptive downsampling includes the steps of: (a) receiving input data having a first spatial resolution and a first grid spacing; (b) resampling the input data using an automatically adjustable kernel; and (c) providing output data having a second spatial resolution and a second grid spacing. The second spatial resolution, advantageously, is substantially constant across the second grid spacing. The input data may include image data, and the output data may include resampled, or downsampled image data. The image data may be images of the Earth at a first spatial resolution and a first grid spacing; the output data may be resampled data of the images of the Earth at a second spatial resolution and a second grid spacing.