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Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image
To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960840/ https://www.ncbi.nlm.nih.gov/pubmed/31888269 http://dx.doi.org/10.3390/s19245559 |
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author | Li, Na Wang, Ruihao Zhao, Huijie Wang, Mingcong Deng, Kewang Wei, Wei |
author_facet | Li, Na Wang, Ruihao Zhao, Huijie Wang, Mingcong Deng, Kewang Wei, Wei |
author_sort | Li, Na |
collection | PubMed |
description | To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91. |
format | Online Article Text |
id | pubmed-6960840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69608402020-01-24 Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image Li, Na Wang, Ruihao Zhao, Huijie Wang, Mingcong Deng, Kewang Wei, Wei Sensors (Basel) Article To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91. MDPI 2019-12-16 /pmc/articles/PMC6960840/ /pubmed/31888269 http://dx.doi.org/10.3390/s19245559 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Na Wang, Ruihao Zhao, Huijie Wang, Mingcong Deng, Kewang Wei, Wei Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image |
title | Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image |
title_full | Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image |
title_fullStr | Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image |
title_full_unstemmed | Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image |
title_short | Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image |
title_sort | improved classification method based on the diverse density and sparse representation model for a hyperspectral image |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960840/ https://www.ncbi.nlm.nih.gov/pubmed/31888269 http://dx.doi.org/10.3390/s19245559 |
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