Transformer Networks for Satellite Image Super-Resolution
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Solution Overview
Problem
Agricultural personnel face challenges in making accurate decisions due to the limitations of satellite imagery, which often captures geographic areas at different times, frequencies, spectral bands, and spatial resolutions, making it difficult to utilize the available data effectively for monitoring crop health and other agricultural purposes.
Innovation Solution
A network of transformer machine learning models is used to generate inferred image data that fills in missing spectral, spatial, and temporal information by analyzing high-elevation images from multiple satellites, allowing for the extrapolation of data to higher resolutions and frequencies, thereby enhancing the ability to infer terrain conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite imagery is used to capture large expanses of land, then the area covered is improved, but the spatial resolution deteriorates
Solution Approach 1:
The patent segments the problem by processing satellite imagery in multiple spectral bands separately, applying super-resolution models to each band independently, then combining the results. This allows high-resolution reconstruction of each spectral component while maintaining the broad area coverage of the original satellite data.
Solution Approach 2:
The patent transitions from two-dimensional satellite imagery to a multi-dimensional representation by incorporating multiple spectral bands (visible, near-infrared, shortwave infrared) and temporal dimensions. The super-resolution model operates in this expanded dimensionality to recover high-resolution data that cannot be obtained from a single satellite pass.
2Duration of action of moving object
If satellite imagery is captured at different times and frequencies, then temporal coverage is improved, but the reliability of data for specific time points deteriorates
Solution Approach 1:
The patent performs preliminary actions by capturing satellite imagery at multiple time points and spectral bands before the specific analysis moment. This accumulated data serves as training material for the super-resolution model, enabling it to reliably infer what would have been captured at any given time point, even when direct observations are unavailable.
Solution Approach 2:
The system uses feedback loops where the super-resolution model continuously refines its predictions by comparing inferred data with available observations. The model learns from discrepancies between predicted and actual data, improving its ability to reliably reconstruct terrain features at specific time points across the entire temporal coverage period.
3Loss of information
If multiple spectral bands are captured, then the information content is improved, but the complexity of processing and utilizing the data deteriorates
Solution Approach 1:
The patent segments the multi-spectral data processing into separate super-resolution models trained for each spectral band. This modular approach allows the system to handle the complexity of multiple bands by processing them independently through specialized models, then combining the high-resolution outputs to achieve comprehensive information recovery.
Data Source
AI summary
Implementations are described herein for using one or more transformer networks to generate inferred image data based on processing image data capturing a particular geographic area during a particular time period, including first image data captured in a first spectral band and at a first spatial (and/or temporal) resolution and second image data captured in a second spectral band and at a second spatial (and/or temporal) resolution. The inferred image data can include second spectral information at the first spatial (and/or temporal) resolution, or vice versa. Thus, the spatial and/or temporal resolution of image data of a certain spectral band can be improved, allowing for more effective usage of satellite imagery in agricultural settings.