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Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network
In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes i...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210679/ https://www.ncbi.nlm.nih.gov/pubmed/30360445 http://dx.doi.org/10.3390/s18103587 |
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author | Li, Chenming Yang, Simon X. Yang, Yao Gao, Hongmin Zhao, Jia Qu, Xiaoyu Wang, Yongchang Yao, Dan Gao, Jianbing |
author_facet | Li, Chenming Yang, Simon X. Yang, Yao Gao, Hongmin Zhao, Jia Qu, Xiaoyu Wang, Yongchang Yao, Dan Gao, Jianbing |
author_sort | Li, Chenming |
collection | PubMed |
description | In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy. |
format | Online Article Text |
id | pubmed-6210679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62106792018-11-02 Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network Li, Chenming Yang, Simon X. Yang, Yao Gao, Hongmin Zhao, Jia Qu, Xiaoyu Wang, Yongchang Yao, Dan Gao, Jianbing Sensors (Basel) Article In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy. MDPI 2018-10-22 /pmc/articles/PMC6210679/ /pubmed/30360445 http://dx.doi.org/10.3390/s18103587 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, Chenming Yang, Simon X. Yang, Yao Gao, Hongmin Zhao, Jia Qu, Xiaoyu Wang, Yongchang Yao, Dan Gao, Jianbing Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network |
title | Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network |
title_full | Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network |
title_fullStr | Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network |
title_full_unstemmed | Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network |
title_short | Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network |
title_sort | hyperspectral remote sensing image classification based on maximum overlap pooling convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210679/ https://www.ncbi.nlm.nih.gov/pubmed/30360445 http://dx.doi.org/10.3390/s18103587 |
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