Real-Time Multi-Spectral Target Detection with Atmospheric Correction
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Solution Overview
Problem
Current multi-spectral target detection systems are unable to achieve real-time atmospheric correction and target detection, requiring offline processing and lacking integration of Machine Learning (ML) and Artificial Intelligence (AI) for automation.
Innovation Solution
A multi-spectral system and method that includes a multi-spectral sensor operating in SWIR mode, calculating exposure times, applying atmospheric correction matrices, and using ML models for real-time target identification and geolocation, with additional sensors for investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline atmospheric correction and analysis are performed by human analysts, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent replaces the mechanical system of manual atmospheric correction and analysis performed by human analysts with an automated computational system. The processing circuitry automatically performs atmospheric correction on multi-spectral imaging data and executes target detection algorithms, eliminating the need for human analysts to manually process each dataset while maintaining measurement precision through systematic computational methods.
Solution Approach 2:
The system enables self-service by allowing the multi-spectral imaging system to automatically correct its own atmospheric effects and detect targets without external human intervention. The processing circuitry applies atmospheric correction models and analysis algorithms autonomously, making the system self-sufficient in generating actionable intelligence from captured imagery.
2Productivity
If real-time target detection is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the target detection process into distinct functional modules that can be processed independently and in parallel. The processing circuitry divides the multi-spectral data cube analysis into separate operations including atmospheric correction, spectral signature matching, and target identification. This modular segmentation enables real-time processing by allowing simultaneous execution of multiple detection algorithms across different spectral bands, thereby improving productivity without overwhelming the system.
Solution Approach 2:
The system performs preliminary atmospheric correction on the multi-spectral data cube before target detection is initiated. By pre-processing the data to remove atmospheric effects and prepare corrected spectral information in advance, the system reduces the computational burden during actual target detection, enabling faster real-time analysis while managing device complexity through staged processing.
Data Source
AI summary
A system for detecting one or more target materials in an un-calibrated multi-spectral data cube comprising a collection of pixels, the system comprising a processing circuitry configured to: obtain: (A) a machine learning model capable of receiving the un-calibrated multi-spectral data cube and determining for at least one pixel of the pixels at least one material indicator, indicative of existence of a given target material of the target materials at the location of the pixel, wherein the machine learning model is trained utilizing a labeled training-data set comprising of a plurality of training records, each training record comprising: (i) a training un-calibrated multi-spectral data cube, and (ii) at least one training material indicator associated with at least one pixel of the training un-calibrated multi-spectral data cube, indicative of existence of the target material at the location of the pixel, and (B) the un-calibrated multi-spectral data cube; and determine for at least one pixel of the pixels of the un-calibrated multi-spectral data cube, at least one material indicator, and a corresponding calibrated multi-spectral data cube, wherein the corresponding calibrated multi-spectral data cube is calculated by utilizing a calibration process and an atmospheric simulator that simulates a plurality of simulated un-calibrated multi-spectral data cubes by simulation of different atmospheric conditions over the calibrated multi-spectral cube.