Repository logo
  • Log In
    New user? Click here to register.Have you forgotten your password?
University College Dublin
    Colleges & Schools
    Statistics
    All of DSpace
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. College of Engineering & Architecture
  3. School of Biosystems and Food Engineering
  4. Biosystems and Food Engineering Research Collection
  5. Spatial-spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images
 
  • Details
Options

Spatial-spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images

Author(s)
Xu, Jun-Li  
Gowen, Aoife  
Uri
http://hdl.handle.net/10197/11769
Date Issued
2020-02
Date Available
2020-12-01T16:11:29Z
Abstract
This work proposes a new method to treat spatial and spectral information interactively. The method extracts spatial features, ie, variogram, gray-level co-occurrence matrix (GLCM), histograms of oriented gradients (HOG), and local binary pattern (LBP) features, from each wavelength image of hypercube and principal component analysis (PCA) is applied on this spatial feature matrix to identify wavelength-dependent variation in spatial patterns. Resultant image is obtained by projecting the score values to the original data. Three datasets, including a synthetic hyperspectral image (Dataset 1), a set of real hyperspectral images of salmon fillets (Dataset 2), and remote-sensing images (Dataset 3), were utilized to evaluate the performance of the proposed method. Results from Dataset 1 showed that the spatial-spectral methods had the potential of reducing baseline offset noise. Dataset 2 revealed that spatial-spectral methods can alleviate noisy pixels with strong signal and reduce shadow effects. In addition, substantial improvements were obtained in case of classification between white stripe and red muscle pixels by using the HOG-based approach with correct classification rate (CCR) of 0.97 compared with the models directly built from raw and standard normal variate (SNV) preprocessed spectra (CCR = 0.94). Samson image of Dataset 3 suggested the flexibility and effectiveness of the proposed method by improving CCR of 0.96 using conventional PCA on SNV pretreated spectra to 0.98 using GLCM-based approach on SNV preprocessed spectra. Overall, experimental results demonstrated that the spatial-spectral methods can improve the results found by using the spectral information alone because of the spatial information provided.
Sponsorship
European Commission - Seventh Framework Programme (FP7)
University College Dublin
Type of Material
Journal Article
Publisher
Wiley
Journal
Journal of Chemometrics
Volume
34
Issue
2
Copyright (Published Version)
2019 Wiley
Subjects

Hyperspectral image

PCA

Spatial integration

Spectral integration

Texture analysis

Classification

Semivariogram

Resolution

DOI
10.1002/cem.3132
Language
English
Status of Item
Peer reviewed
ISSN
0886-9383
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

Final_version.docx

Size

21.17 MB

Format

Unknown

Checksum (MD5)

4d6ac1a028cfadf0fd9501432022f616

Owning collection
Biosystems and Food Engineering Research Collection
Mapped collections
Institute of Food and Health Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

For all queries please contact research.repository@ucd.ie.

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement