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Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification
The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this pap...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151123/ https://www.ncbi.nlm.nih.gov/pubmed/34068823 http://dx.doi.org/10.3390/mi12050545 |
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author | Li, Chenming Qiu, Zelin Cao, Xueying Chen, Zhonghao Gao, Hongmin Hua, Zaijun |
author_facet | Li, Chenming Qiu, Zelin Cao, Xueying Chen, Zhonghao Gao, Hongmin Hua, Zaijun |
author_sort | Li, Chenming |
collection | PubMed |
description | The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this paper, a new HSI model called local and hybrid dilated convolution fusion network (LDFN) was proposed, which fuses the local information of details and rich spatial features by expanding the perception field. The details of our local and hybrid dilated convolution fusion network methods are as follows. First, many operations are selected, such as standard convolution, average pooling, dropout and batch normalization. Then, fusion operations of local and hybrid dilated convolution are included to extract rich spatial-spectral information. Last, different convolution layers are gathered into residual fusion networks and finally input into the softmax layer to classify. Three widely hyperspectral datasets (i.e., Salinas, Pavia University and Indian Pines) have been used in the experiments, which show that LDFN outperforms state-of-art classifiers. |
format | Online Article Text |
id | pubmed-8151123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81511232021-05-27 Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification Li, Chenming Qiu, Zelin Cao, Xueying Chen, Zhonghao Gao, Hongmin Hua, Zaijun Micromachines (Basel) Article The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this paper, a new HSI model called local and hybrid dilated convolution fusion network (LDFN) was proposed, which fuses the local information of details and rich spatial features by expanding the perception field. The details of our local and hybrid dilated convolution fusion network methods are as follows. First, many operations are selected, such as standard convolution, average pooling, dropout and batch normalization. Then, fusion operations of local and hybrid dilated convolution are included to extract rich spatial-spectral information. Last, different convolution layers are gathered into residual fusion networks and finally input into the softmax layer to classify. Three widely hyperspectral datasets (i.e., Salinas, Pavia University and Indian Pines) have been used in the experiments, which show that LDFN outperforms state-of-art classifiers. MDPI 2021-05-10 /pmc/articles/PMC8151123/ /pubmed/34068823 http://dx.doi.org/10.3390/mi12050545 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Chenming Qiu, Zelin Cao, Xueying Chen, Zhonghao Gao, Hongmin Hua, Zaijun Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification |
title | Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification |
title_full | Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification |
title_fullStr | Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification |
title_full_unstemmed | Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification |
title_short | Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification |
title_sort | hybrid dilated convolution with multi-scale residual fusion network for hyperspectral image classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151123/ https://www.ncbi.nlm.nih.gov/pubmed/34068823 http://dx.doi.org/10.3390/mi12050545 |
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