Hyperspectral Spectral-Shape Learning for Interpretable Pixel Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for hyperspectral data classification ignore spectral features and fail to interpret them semantically, treating pixel vectors as one-dimensional vectors and performing convolutions that result in non-interpretable spectral feature extraction.
Innovation Solution
Transform pixel vectors into two-dimensional spectral shape space and perform convolutions on 2D graph images to learn spectral features using Deep Convolutional Neural Networks (DCNN), focusing on spectral features without spatial context, and utilize DCNN architectures that process 2D visual representations to classify pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel vectors are treated as n-dimensional vectors with separate convolution over depth information, then spectral features can be extracted, but the semantics of learned spectral features are ignored and not interpretable
Solution Approach 1:
The patent transforms 1D spectral vectors into 2D spectral shape representations by plotting spectral values against wavelength positions. This dimensional transformation enables the DCNN to learn spatial patterns within the spectral domain, making spectral features visually interpretable while maintaining extraction accuracy. The 2D spectral shapes preserve semantic information about spectral characteristics such as absorption peaks and reflection patterns.
2Productivity
If DCNN processes 1D spectral vectors directly, then computational efficiency is maintained, but spectral feature semantics are lost and not interpretable
Solution Approach 1:
The patent creates a visual copy of spectral data by generating 2D spectral shape images from 1D spectral vectors. This copying approach allows the DCNN to process familiar 2D image data using established convolutional operations, maintaining computational efficiency while gaining interpretability. The spectral shapes serve as visual representations that preserve all original spectral information in an interpretable format.
3Adaptability or versatility
If spectral features are stacked with spatial convolutional features, then comprehensive feature representation is achieved, but spectral feature semantics are completely ignored
Solution Approach 1:
The patent applies dimensional transformation specifically to the spectral feature extraction process, creating 2D spectral shapes that can be processed by DCNN. This enables the network to learn hierarchical patterns within spectral data while maintaining semantic interpretability. The resulting spectral features can then be effectively combined with spatial features, as both are now in a format that preserves meaningful information.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
Hyperspectral data associated with hyperspectral images received for any Region of Interest (ROI) is in form of number of pixel vectors. Unlike conventional methods in the art that treat this pixel vector as a time series, the embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Learning from pixel vectors directly may not capture the spectral details efficiently. The intuition is to learn the spectral features as represented by the shape of a spectrum or in other words the features which a spectroscopy expert uses to interpret the spectrum. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN now learn edges, arcs, arcs segments and the other shape features of the spectrum