Hyperspectral Analysis Computer Device for Mission-Specific Training
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Solution Overview
Problem
The training of hyperspectral systems is expensive and time-consuming, and requires additional training to meet mission-specific requirements, as they need to differentiate items of interest from background details effectively.
Innovation Solution
A hyperspectral analysis computer device and method that utilize machine learning techniques to generate spectral bands and simulate hyperspectral images based on mission parameters, allowing for efficient training and analysis by determining the appropriate spectral bands and simulating various conditions to improve detection probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral systems are trained using traditional methods with large plurality of hyperspectral images, then the system can learn to differentiate items of interest from background, but the training process becomes expensive and time-consuming
Solution Approach 1:
The patent creates synthetic hyperspectral images by combining spectral signatures of items of interest with background images. Instead of relying solely on expensive real-world training data, the system generates realistic training samples through computational synthesis, significantly reducing the time and cost required to accumulate sufficient training data while maintaining differentiation accuracy
Solution Approach 2:
The system performs preliminary spectral analysis to identify and extract spectral signatures of items of interest before generating the full set of training images. By pre-processing and preparing spectral characteristics in advance, the system streamlines the subsequent image generation process and reduces overall training preparation time
2Adaptability or versatility
If hyperspectral systems are trained with generic data, then the system can recognize general features, but additional training is required to meet mission-specific requirements
Solution Approach 1:
The patent generates spectral signatures and training images tailored to specific mission requirements by incorporating local environmental characteristics, target properties, and mission parameters. This localized customization allows the system to adapt to different missions without requiring complete retraining, as the synthesis process can be adjusted to match specific operational contexts
Solution Approach 2:
The system dynamically adjusts the spectral signatures and image generation parameters based on the specific mission requirements. By making the training data generation process adaptive and configurable, the system can efficiently transition between different mission types without fixed structural changes to the hyperspectral system itself
3Measurement precision
If more spectral bands are generated for analysis, then the detection accuracy of items of interest improves, but the processing complexity and time increase
Solution Approach 1:
The patent extracts and focuses on the most informative spectral bands by analyzing and identifying key spectral features that are most relevant for detecting items of interest. By selecting only the critical spectral regions rather than processing all available bands uniformly, the system maintains high detection accuracy while reducing the computational burden and processing time
Data Source
AI summary
A hyperspectral analysis computer device is provided. The hyperspectral analysis computer device includes at least one processor in communication with at least one memory device. The hyperspectral analysis computer device is configured to store a plurality of spectral analysis data, receive at least one background item and at least one item to be detected from a user, generate one or more spectral bands for analysis based on the at least one background item, the at least one item to be detected, and the stored plurality of spectral analysis data, receive one or more mission parameters from the user, and determine a probability of success based on the one or more mission parameters and the generated one or more spectral bands.