<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (<italic>ie</italic> indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.</p>
</abstract>

Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (ie indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.

Multiscale filter-based hyperspectral image classification with PCA and SVM

Department of Computer Science and Software Engineering

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Multiscale filter-based hyperspectral image classification with PCA and SVM"}

Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different...

Multiscale filter-based hyperspectral image classification with PCA and SVM

Online veröffentlicht: 18. März 2021

Seitenbereich: 40 - 45

Eingereicht: 01. Nov. 2020

DOI: https://doi.org/10.2478/jee-2021-0006

Schlüsselwörter
hyperspectral image classification, 2 D filter, 2 D convolution, support vector machines (SVM)

© 2021 Guang Yi Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Multiscale filter-based hyperspectral image classification with PCA and SVM

Online veröffentlicht: 18. März 2021

Seitenbereich: 40 - 45

Eingereicht: 01. Nov. 2020

DOI: https://doi.org/10.2478/jee-2021-0006

Schlüsselwörterhyperspectral image classification, 2 D filter, 2 D convolution, support vector machines (SVM)

© 2021 Guang Yi Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
hyperspectral image classification, 2 D filter, 2 D convolution, support vector machines (SVM)