An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+
DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral ima...
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MDPI AG
2021-06-01
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Online Access: | https://www.mdpi.com/2076-3417/11/12/5703 |
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author | Yifan Si Dawei Gong Yang Guo Xinhua Zhu Qiangsheng Huang Julian Evans Sailing He Yaoran Sun |
author_facet | Yifan Si Dawei Gong Yang Guo Xinhua Zhu Qiangsheng Huang Julian Evans Sailing He Yaoran Sun |
author_sort | Yifan Si |
collection | DOAJ |
description | DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks. |
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format | Article |
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institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-03-10T10:14:43Z |
publishDate | 2021-06-01 |
publisher | MDPI AG |
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series | Applied Sciences |
spelling | doaj.art-73aa45f496814eeaab440eed7984288e2023-11-22T00:53:29ZengMDPI AGApplied Sciences2076-34172021-06-011112570310.3390/app11125703An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+Yifan Si0Dawei Gong1Yang Guo2Xinhua Zhu3Qiangsheng Huang4Julian Evans5Sailing He6Yaoran Sun7National Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaNational Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaNational Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaResearch Institute of Zhejiang University-Taizhou, Taizhou 318000, ChinaNational Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaNational Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaNational Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaNational Engineering Research Center for Optical Instruments, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, ChinaDeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.https://www.mdpi.com/2076-3417/11/12/5703DeepLab v3+hyperspectral imagery classificationprincipal component analysisfeatures fusionsupport vector machine |
spellingShingle | Yifan Si Dawei Gong Yang Guo Xinhua Zhu Qiangsheng Huang Julian Evans Sailing He Yaoran Sun An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+ Applied Sciences DeepLab v3+ hyperspectral imagery classification principal component analysis features fusion support vector machine |
title | An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+ |
title_full | An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+ |
title_fullStr | An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+ |
title_full_unstemmed | An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+ |
title_short | An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+ |
title_sort | advanced spectral spatial classification framework for hyperspectral imagery based on deeplab v3 |
topic | DeepLab v3+ hyperspectral imagery classification principal component analysis features fusion support vector machine |
url | https://www.mdpi.com/2076-3417/11/12/5703 |
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