A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy
A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the ba...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2021/5592323 |
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doaj-930a4bc5abcc45249f76319caf18273a2021-07-05T00:02:37ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52732021-01-01202110.1155/2021/5592323A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection StrategyNian Chen0Kezhong Lu1Hao Zhou2School of Big Data and Artificial IntelligenceSchool of Big Data and Artificial IntelligenceSchool of Big Data and Artificial IntelligenceA band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first. During the implementation, we normalize the local density and relative distance and utilize the dynamic cutoff distance to weaken the influence of density so that the selection is more likely to be carried out in scattered clusters than in high-density ones. After that, we conduct a further selection in the candidate set using mRMR strategy and comprehensive measurement of information (CMI), and the eventual winners will be selected into the target set. Compared with other six state-of-the-art unsupervised algorithms on three real-world HSI data sets, the results show that TLS can group the bands with lower correlation and richer information and has obvious advantages in indicators of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.http://dx.doi.org/10.1155/2021/5592323 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nian Chen Kezhong Lu Hao Zhou |
spellingShingle |
Nian Chen Kezhong Lu Hao Zhou A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy Computational Intelligence and Neuroscience |
author_facet |
Nian Chen Kezhong Lu Hao Zhou |
author_sort |
Nian Chen |
title |
A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy |
title_short |
A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy |
title_full |
A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy |
title_fullStr |
A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy |
title_full_unstemmed |
A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy |
title_sort |
search method for optimal band combination of hyperspectral imagery based on two layers selection strategy |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5273 |
publishDate |
2021-01-01 |
description |
A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first. During the implementation, we normalize the local density and relative distance and utilize the dynamic cutoff distance to weaken the influence of density so that the selection is more likely to be carried out in scattered clusters than in high-density ones. After that, we conduct a further selection in the candidate set using mRMR strategy and comprehensive measurement of information (CMI), and the eventual winners will be selected into the target set. Compared with other six state-of-the-art unsupervised algorithms on three real-world HSI data sets, the results show that TLS can group the bands with lower correlation and richer information and has obvious advantages in indicators of overall accuracy (OA), average accuracy (AA), and Kappa coefficient. |
url |
http://dx.doi.org/10.1155/2021/5592323 |
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