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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing.

Yu-Hang Li^1,2, Xin Tan¹, Wei Zhang^1,2

¹Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China.

Frontiers in Plant Science

|March 8, 2021

Summary

This study introduces advanced hyperspectral image preprocessing techniques, including segmentation, correction, and spatial-spectral denoising, to significantly boost classification accuracy. The novel two-dimensional Savitzky-Golay (TSG) filter method achieved over 98% accuracy, outperforming traditional approaches.

Keywords:

classification recognition double standard reflectance plates hyperspectral image image segmentation preprocessing spatial-spectral dimension combined filtering

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Area of Science:

Remote Sensing
Image Processing
Data Science

Background:

Hyperspectral image classification accuracy is often limited by noise and spectral variability.
Effective preprocessing is crucial for extracting meaningful information from hyperspectral data.

Purpose of the Study:

To enhance hyperspectral image classification accuracy through improved preprocessing techniques.
To develop and evaluate a novel spatial-spectral denoising method for hyperspectral images.

Main Methods:

Image segmentation using spectral angle and principal component analysis (PCA).
Image correction utilizing standard reflectance plates for spectral calibration.
Development and application of a two-dimensional Savitzky-Golay (TSG) filter for joint spatial-spectral denoising.

Main Results:

Achieved excellent hyperspectral image segmentation results.
Image correction reduced mean square error to <0.0001, improving classification accuracy compared to black-and-white correction.
The TSG filter effectively reduced noise while preserving original image features.
TSG filter-based classification models achieved >98% accuracy.

Conclusions:

The proposed hyperspectral image preprocessing methods, particularly the TSG filter, significantly improve classification accuracy.
The TSG filter offers a robust solution for denoising hyperspectral images while retaining critical spectral and spatial information.