Hyperspectral Image Segmentation Using Region-Specific Band Profiles
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Solution Overview
Problem
Hyperspectral images, which provide significantly more information than traditional color images, are not efficiently processed for image segmentation tasks due to the large amount of data and varying relevance of wavelength bands for different region types, leading to inefficiency and reduced accuracy.
Innovation Solution
A computer system generates and uses profiles that specify subsets of wavelength bands for accurate and efficient segmentation of different object types by selecting bands that best indicate region boundaries, using data-driven analysis and synthetic bands to enhance image data, reducing computational cost and improving segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all wavelength bands of hyperspectral images are used for segmentation, then more information is available for analysis, but computational cost increases and processing efficiency decreases
Solution Approach 1:
The patent extracts and selects only the most relevant wavelength bands from the full hyperspectral dataset for each specific segmentation task. By identifying and removing redundant or less informative bands, the system maintains adequate information for accurate segmentation while significantly reducing the data volume that requires computational processing.
Solution Approach 2:
The patent applies different subsets of wavelength bands tailored to specific region types and object characteristics. Instead of using a uniform approach across all segmentation tasks, the system optimizes the information content locally by selecting bands that are most informative for each particular segmentation scenario, thereby improving efficiency without sacrificing necessary information.
2Ease of operation
If all wavelength bands are processed uniformly, then consistent processing is simplified, but segmentation accuracy decreases for different region types
Solution Approach 1:
The patent segments the wavelength bands into different subsets based on region types and object characteristics. By dividing the processing into task-specific band subsets, the system achieves higher segmentation accuracy for different region types while maintaining operational simplicity through automated profile selection.
Solution Approach 2:
The patent introduces dynamic adaptability by selecting different wavelength band subsets based on the specific segmentation task and region type. The system dynamically adjusts which bands are processed based on pre-defined profiles, allowing optimal performance for different scenarios while maintaining ease of operation through automated profile matching.
3Measurement precision
If data-driven analysis is performed to identify optimal band combinations, then segmentation accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent performs data-driven analysis in advance to pre-identify optimal wavelength band combinations for different region types and object characteristics. These profiles are stored and can be directly applied during segmentation tasks without requiring real-time computational analysis, thus achieving high segmentation accuracy while minimizing processing time during actual operation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved image segmentation using hyperspectral imaging. In some implementations, a system obtains image data of a hyperspectral image, the image data comprising image data for each of multiple wavelength bands. The system accesses stored segmentation profile data for a particular object type that indicates a predetermined subset of the wavelength bands designated for segmenting different region types for images of an object of the particular object type. The system segments the image data into multiple regions using the predetermined subset of the wavelength bands specified in the stored segmentation profile data to segment the different region types. The system provides output data indicating the multiple regions and the respective region types of the multiple regions.