Adaptive Spatial-Spectral Processing for Hyperspectral Point Target Detection
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Solution Overview
Problem
Conventional hyperspectral imaging sensors have large physical pixels due to their design, leading to limited resolution, high cost, and inadequate performance in detecting point targets blurred over multiple pixels, requiring higher area coverage, resolution, and signal-to-noise ratio, which existing systems cannot provide.
Innovation Solution
The development of hyperspectral imaging sensors with spatially smaller pixels than the point spread function, combined with adaptive spatial spectral processing, allows for detecting targets with spatial extents less than or equal to one pixel, enhancing signal-to-noise ratio and reducing clutter by using adaptive weight vectors and background spectral statistics for improved target detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional hyperspectral imaging sensors use large physical pixels to capture sufficient energy, then signal-to-noise ratio is improved, but spatial resolution deteriorates
Solution Approach 1:
The patent divides the detection task into two stages: first, a conventional sensor captures blurred spectral data from point targets spread across multiple pixels; second, a specialized processor segments and recombines the spectral information from multiple pixels to reconstruct the point target signal, effectively achieving high resolution without requiring large individual pixels
Solution Approach 2:
The patent introduces a specialized processing unit as an intermediary between the conventional sensor and the final output. This processor acts as a mediator that takes the blurred multi-pixel data and applies algorithms to recover the point target information, enabling high resolution without sacrificing signal-to-noise ratio
2Manufacturing precision
If conventional hyperspectral imaging sensors use large apertures to improve resolution, then spatial resolution is improved, but system cost increases geometrically
Solution Approach 1:
The patent replaces the mechanical approach of using large apertures with a computational approach. Instead of physically enlarging the aperture to improve resolution, the system uses a smaller aperture combined with specialized processing algorithms that mathematically recover high-resolution information from the captured data
3Ease of operation
If conventional processing algorithms are used with blurred point targets, then processing simplicity is maintained, but detection accuracy deteriorates
Solution Approach 1:
The patent incorporates preliminary action by designing a processing pipeline that first captures the blurred spectral data with a conventional sensor, then applies pre-programmed algorithms specifically tailored for point target recovery. The processing is simplified through predetermined computational steps that automatically reconstruct the point target signal from the multi-pixel blurred input
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the detection of targets with spatial extents smaller than the point spread function, reduces payload mass by 30-70%, and improves signal-to-noise ratio and area coverage, making it suitable for applications that conventional systems cannot handle.
Implementation Method 1
The spatial response function is the full width, half maximum of the convolution of the optical point spread function (which is dependent on diffraction, optical aberrations, and slit size) with the detector function
Implementation Method 2
conventional hyperspectral imaging sensors are configured to have sensor pixels that are approximately the same size or slightly larger than the point spread function of the sensor
Data Source
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AI summary
A hyperspectral imaging sensor and an adaptive spatial spectral processing filter capable of detecting, identifying, and/or classifying targets having a spatial extent of one pixel or less includes a sensor that may be oversampled such that a pixel is spatially smaller than the optical blur or point spread function of the sensor. Adaptive spatial spectral processing may be performed on hyperspectral image data to detect targets having spectral features that are known a priori, and/or that are anomalous compared to nearby pixels. Further, the adaptive spatial spectral processing may recover target energy spread over multiple pixels and reduce background clutter to increase the signal-to-noise ratio.