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ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned...

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Autores principales: Li, Na, Xu, Zhaopeng, Zhao, Huijie, Huang, Xinchen, Li, Zhenhong, Drummond, Jane, Wang, Daming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877212/
https://www.ncbi.nlm.nih.gov/pubmed/29510547
http://dx.doi.org/10.3390/s18030780
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author Li, Na
Xu, Zhaopeng
Zhao, Huijie
Huang, Xinchen
Li, Zhenhong
Drummond, Jane
Wang, Daming
author_facet Li, Na
Xu, Zhaopeng
Zhao, Huijie
Huang, Xinchen
Li, Zhenhong
Drummond, Jane
Wang, Daming
author_sort Li, Na
collection PubMed
description The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.
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spelling pubmed-58772122018-04-09 ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery Li, Na Xu, Zhaopeng Zhao, Huijie Huang, Xinchen Li, Zhenhong Drummond, Jane Wang, Daming Sensors (Basel) Article The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively. MDPI 2018-03-05 /pmc/articles/PMC5877212/ /pubmed/29510547 http://dx.doi.org/10.3390/s18030780 Text en © 2018 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
Xu, Zhaopeng
Zhao, Huijie
Huang, Xinchen
Li, Zhenhong
Drummond, Jane
Wang, Daming
ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery
title ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery
title_full ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery
title_fullStr ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery
title_full_unstemmed ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery
title_short ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery
title_sort isbdd model for classification of hyperspectral remote sensing imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877212/
https://www.ncbi.nlm.nih.gov/pubmed/29510547
http://dx.doi.org/10.3390/s18030780
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