Hyperspectral Image Segmentation Using Region-Specific Band Profiles
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Solution Overview
Problem
Hyperspectral images, with their vast amount of data across multiple wavelength bands, are not efficiently processed for image segmentation tasks due to irrelevant or noisy bands, leading to inefficiency and reduced accuracy in segmentation.
Innovation Solution
A computer system generates profiles that specify subsets of wavelength bands for different object and region types, using data-driven analysis to select bands that accurately segment regions while minimizing computational cost, and applies synthetic bands to enhance segmentation.
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 segments the hyperspectral image data by dividing it into multiple wavelength bands. Each band is processed separately through neural network layers, allowing the system to selectively attend to relevant spectral information while reducing the computational burden of processing all bands simultaneously. This segmentation of spectral data enables efficient processing while preserving important information.
Solution Approach 2:
The patent extracts only the most relevant wavelength bands for segmentation tasks using attention mechanisms. The system identifies and extracts key spectral features that are most informative for boundary detection, discarding redundant or noisy bands. This extraction process reduces computational cost while maintaining segmentation accuracy.
2Loss of information
If all wavelength bands are processed, then complete spectral information is utilized, but segmentation accuracy decreases due to noise from irrelevant bands
Solution Approach 1:
The patent applies local quality by allowing different parts of the spectral data (different wavelength bands) to have different levels of importance for segmentation. The attention mechanism assigns different weights to different bands based on their relevance to specific segmentation tasks. This enables the system to focus on locally optimal spectral regions that provide the most useful information for detecting object boundaries.
Solution Approach 2:
The patent processes only a subset of wavelength bands that are most relevant for segmentation, rather than processing all bands equally. The attention mechanism selectively activates processing for specific spectral regions while suppressing or ignoring irrelevant bands. This partial action approach improves segmentation accuracy by avoiding noise from excessive or irrelevant spectral information.
3Productivity
If traditional RGB bands are used, then processing is computationally efficient, but segmentation accuracy is reduced compared to hyperspectral data
Solution Approach 1:
The patent creates a universal processing framework that can handle both traditional RGB images and hyperspectral images through the same neural network architecture. The system is designed to accept variable numbers of input bands and automatically adapt its processing. This multi-functionality allows the same model to achieve high accuracy on both RGB and hyperspectral data while maintaining computational efficiency.
Solution Approach 2:
The patent changes the spectral parameter by processing data at multiple scales of spectral resolution. The system can operate with three bands (RGB) or many more bands (hyperspectral) by adjusting the input configuration. The attention mechanism dynamically adjusts which spectral parameters (bands) are most relevant, allowing the system to achieve high accuracy across different spectral resolutions without requiring separate models.
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.