Image Processing Device SNR Prediction for Vegetation Index Accuracy
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Solution Overview
Problem
Current image sensors used in remote sensing of farm fields face challenges in achieving accurate vegetation index calculations due to varying environmental conditions such as sun position, weather, and photography angles, leading to significant signal-to-noise ratio (SNR) fluctuations, which affect the quality of sensing results.
Innovation Solution
An information processing device that predicts the SNR of image data and performs SNR adjustment by averaging neighboring pixels or applying spatial noise reduction, allowing for more accurate vegetation index calculations, even under changing environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image sensors with standard SNR performance are used for remote sensing, then device complexity and cost are kept low, but measurement precision deteriorates due to SNR fluctuations from environmental changes
Solution Approach 1:
The patent performs preliminary SNR prediction and adjustment on image data before calculating vegetation indices. By predicting the SNR of captured images and adjusting them in advance, the system ensures accurate measurement results without requiring complex hardware modifications during the sensing process
Solution Approach 2:
The patent introduces an intermediate processing step that acts as a mediator between image capture and vegetation index calculation. The SNR prediction and adjustment process serves as an intermediary that corrects image quality issues without requiring direct hardware changes, thus resolving the contradiction between measurement precision and device complexity
2Reliability
If automatic exposure control is used to maintain signal level, then ease of operation improves, but reliability deteriorates because sudden weather changes cannot be reliably responded to
Solution Approach 1:
The patent implements a feedback mechanism where the SNR of captured images is predicted and used to adjust subsequent imaging parameters or apply corrections. This feedback loop allows the system to adapt to sudden weather changes by continuously monitoring and adjusting based on actual SNR measurements, improving reliability while maintaining ease of operation
Solution Approach 2:
The system performs preliminary SNR assessment and adjustment before final vegetation index calculation, preparing the image data in advance to account for potential weather-related SNR variations. This preliminary action ensures that even if sudden weather changes occur, the measurement reliability is maintained through pre-computed corrections
3Area of stationary object
If multiple captured images are synthesized to cover wide areas, then area of coverage increases, but measurement precision deteriorates due to environmental variations across different imaging conditions
Solution Approach 1:
The patent applies local quality by performing SNR prediction and adjustment on each captured image individually before synthesis. By treating each image's SNR characteristics separately and applying localized corrections, the system maintains measurement precision across different areas while still achieving wide coverage through multi-image synthesis
Solution Approach 2:
The patent segments the wide-area imaging task into multiple individual image captures, each undergoing separate SNR prediction and adjustment. This segmentation allows precise handling of each image's specific environmental conditions while combining them to achieve comprehensive area coverage, resolving the contradiction between coverage area and measurement precision
Data Source
AI summary
Disclosed is an information processing device, comprising: acquisition circuitry configured to acquire image data including a frame; and index calculation circuitry configured to: determine an average value of the frame, and predict a signal-to-noise ratio of the image data based on the average value of the frame.