Hyperspectral Detection System Context-Image Fusion
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Solution Overview
Problem
Conventional hyperspectral imaging systems require extensive data collection and processing time, which is not necessary for all applications, and are often limited by computing power in mobile or hand-held devices, making real-time data availability challenging for 'Go/No-Go' spectral signature detection.
Innovation Solution
A hyperspectral detection system that combines a context camera and an imaging spectrometer, allowing for real-time data fusion and processing by capturing only a small region of spectral data within the context image, reducing data points and processing time, and enabling immediate feedback and portability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hyperspectral imaging systems collect complete hyper-cube data for the entire scene, then comprehensive spectral information is obtained, but acquisition time increases to tens of seconds and processing becomes too slow for real-time detection
Solution Approach 1:
The system extracts only the spectral data from the region of interest (ROI) rather than collecting spectral information for the entire scene. The context camera identifies the ROI, and only that specific area's spectral signature is captured by the imaging spectrometer, eliminating unnecessary data collection from other areas.
Solution Approach 2:
The imaging process is segmented into two stages: first, a context camera captures a wide-field image to identify the region of interest; second, only the spectral data from that specific ROI is collected. This segmentation allows the system to obtain necessary spectral information without the time penalty of scanning the entire scene.
2Measurement precision
If hyperspectral systems capture complete hyper-cube data with high spatial and spectral resolution, then detailed spectral analysis is enabled, but data size becomes extremely large requiring substantial computing power
Solution Approach 1:
The system extracts only the spectral data from the region of interest (ROI) rather than collecting spectral information for the entire scene. The context camera identifies the ROI, and only that specific area's spectral signature is captured by the imaging spectrometer, eliminating unnecessary data collection from other areas.
Solution Approach 2:
The system performs partial spectral imaging by capturing spectral data only for the region of interest rather than the entire scene. This partial action provides sufficient spectral information for detection applications without the computational burden of processing complete hyper-cube data.
3Reliability
If conventional hyperspectral systems process complete spectral data cubes, then accurate spectral signature verification is achieved, but processing time prevents real-time feedback for mobile or hand-held devices
Solution Approach 1:
The system extracts only the spectral data from the region of interest (ROI) rather than collecting spectral information for the entire scene. The context camera identifies the ROI, and only that specific area's spectral signature is captured by the imaging spectrometer, eliminating unnecessary data collection from other areas.
Solution Approach 2:
The system performs partial spectral imaging by capturing spectral data only for the region of interest rather than the entire scene. This partial action provides sufficient spectral information for detection applications without the computational burden of processing complete hyper-cube data.
Data Source
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Figure 3A~4A
Figure 4B~4C
AI summary
Hyperspectral detector systems and methods for spectrally analyzing a scene are disclosed. The methods include capturing a context image and a single-column spectral image that falls within the context image. The spectral image is panned over the scene and within the context image to capture spectral signatures within the scene. The spectral signatures are compared to reference spectral signatures, and the locations of the one or more spectral signatures are marked. The systems and methods obviate the need to store and process large amounts of spectral data and allow for real-time display of the fused context image and spectral image, along with the marked locations of matched spectral signatures.