Camouflaged Object Classification With Hyperspectral Material Verification
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Solution Overview
Problem
Existing image classification methods, particularly using RGB images, struggle with false classifications due to camouflaged objects in real-world environments, making it difficult to accurately detect and verify objects.
Innovation Solution
Utilizing a hyperspectral imaging system to construct a semantic materials map by labeling each pixel with its hyperspectral signature, followed by clustering and verifying the classified object based on expected materials, enhancing classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RGB image classification is used, then processing speed is maintained, but classification accuracy deteriorates due to camouflaged objects
Solution Approach 1:
The patent combines RGB imaging and hyperspectral imaging into a dual-camera system. The RGB camera provides standard visual information while the hyperspectral camera captures material-specific spectral signatures. By merging these two imaging modalities, the system achieves both high classification accuracy through material verification and maintained processing speed through efficient data fusion techniques.
Solution Approach 2:
The patent introduces a semantic materials map as an intermediary structure that bridges RGB image classification and hyperspectral material verification. This map stores verified material information from hyperspectral data and uses it to correct or validate RGB-based classifications, effectively mediating between the two imaging systems to improve accuracy without requiring full hyperspectral processing for all objects.
2Reliability
If hyperspectral imaging is used for material identification, then detection capability improves for camouflaged objects, but processing time increases
Solution Approach 1:
The patent performs preliminary classification using the RGB image and trained classifier before conducting detailed material verification with hyperspectral data. Objects that can be confidently identified through RGB imaging alone are processed quickly without hyperspectral analysis. Only objects requiring material verification (such as camouflaged objects or those with ambiguous RGB classifications) undergo the time-consuming hyperspectral analysis, thus reducing overall processing time while maintaining high detection reliability.
Solution Approach 2:
The patent applies partial hyperspectral analysis by using the semantic materials map to verify only the material composition of suspected objects rather than analyzing all pixels in the entire image. This selective approach allows the system to maintain high reliability for critical detections while minimizing processing time by avoiding unnecessary hyperspectral processing for clearly identifiable objects.
3Difficulty of detecting and measuring
If edge detection is performed on camouflaged objects, then object localization is attempted, but detection capability deteriorates due to background blending
Solution Approach 1:
The patent exploits color and spectral changes at the material level rather than relying solely on visual color and edge detection. By capturing hyperspectral signatures across multiple frequency bands (visible, infrared, ultraviolet), the system can detect material composition changes that are not visible to the human eye or standard RGB cameras. This allows accurate detection of camouflaged objects through their unique spectral fingerprints even when their visual appearance blends with the background.
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
Improves object classification accuracy by distinguishing materials through hyperspectral signatures, effectively identifying and validating objects, even when camouflaged, thereby improving autonomous system operations.
Implementation Method 1
a hyperspectral image of the real-world environment from a hyperspectral camera mounted to the entity
Data Source
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AI summary
The present disclosure relates to a computer-implemented method of classifying an object from a real-world environment in which an entity operates. The computer-implemented method comprises: receiving an image of the real-world environment from a camera mounted to the entity, and a hyperspectral image of the real-world environment from a hyperspectral camera mounted to the entity; inputting the image to a trained image classifier to classify an object from the image; constructing a semantic materials map of the real-world environment by labelling each pixel of the hyperspectral image with a material based on its hyperspectral signature; identifying the classified object in the semantic materials map; and verifying the classified object as one of valid and invalid based on whether the material of the object in the semantic materials map matches an expected material for the classified object.