Remote Sensing Grid Fusion for Higher Geometric Resolution
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Solution Overview
Problem
Existing remote sensing technologies face limitations in achieving high geometric resolution due to factors such as low sensor sensitivity, narrow spectral range sensitivity, large swath widths, and signal-to-noise ratio issues, leading to data transmission challenges and spectral distortions in fusion methods like pan-sharpening.
Innovation Solution
A method and arrangement that combines geometrically shifted remote sensing data sets from multiple sensors, applying a physically based energy/power balancing and mathematical modeling to enhance geometric resolution without altering spectral characteristics, using overlapping sub-areas to derive higher-resolution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pan-sharpening fusion techniques are used to improve geometric resolution, then geometric resolution is improved, but spectral distortion occurs
Solution Approach 1:
The method segments the scene into multiple overlapping sub-areas using geometric grids from different sensor data sets. By dividing the scene into discrete sub-areas with defined boundaries, the patent enables precise control over how data from multiple sources is combined, allowing geometric resolution enhancement while preserving spectral information in each sub-area through energy balancing constraints.
Solution Approach 2:
The patent applies parameter changes by enforcing energy/power balance constraints during the fusion process. It modifies the fusion approach to maintain physical consistency of radiometric parameters across different data sets, ensuring that spectral characteristics are preserved while geometric resolution is improved through the derivation of function values for overlapping sub-areas.
2Measurement precision
If sensor sensitivity is increased to achieve high geometric resolution, then geometric resolution is improved, but device complexity and cost increase
Solution Approach 1:
The patent merges multiple remote sensing data sets with different geometric resolutions into a single high-resolution data set. By combining data from multiple sensors or multiple acquisitions, the method achieves high geometric resolution without requiring a single high-performance sensor, thus reducing device complexity and cost while maintaining measurement precision.
Solution Approach 2:
The patent adds a temporal or data-source dimension to resolve the geometric resolution issue. Instead of relying solely on sensor performance in a single dimension, it utilizes multiple data sets acquired at different times or from different sensors, combining them through energy balancing to achieve high resolution without increasing individual sensor complexity.
3Measurement precision
If data transmission capacity is increased to handle high-resolution data, then geometric resolution is improved, but transmission cost and time increase
Solution Approach 1:
The patent applies partial action by processing and fusing data in a staged manner. Instead of transmitting and processing all raw high-resolution data simultaneously, it performs energy balancing and function value derivation on overlapping sub-areas, reducing the overall data transmission burden while maintaining high geometric resolution in the final product.
Data Source
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AI summary
The invention relates to a method for improving the geometric resolution of remote sensing data, wherein at least two output sets of remote sensing data of a scene are received or are available, wherein each of the output sets has function values for associated grid elements (G1, G2, G3, G4, H1) of a geometric grid of a scene acquisition, and wherein the function values correspond to radiation emanating from the scene and incident on the associated grid element, wherein the geometric grids of the at least two output sets are geometrically offset from one another, such that in an overlap area of the geometric grids of the at least two output sets, overlap sub-areas (U1, U2, U3) are formed, comprising the overlap area, the geometric size of which is smaller than the geometric size of the grid elements (G1, G2, G3, G4, H1) of the at least two output sets.- for each of the multiple overlapping sub-areas, a derived function value is formed, which is formed from the function values of those grid elements (G1, G2, G3, G4, H1) of the at least two original sets, - a surface integral of the derived function values over the overlap area for each of the original sets is equal to a surface integral of the function values of the grid elements (G1, G2, G3, G4, H1) of the original set.