Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images...

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Autores principales:	Tianyu Zhang, Cuiping Shi, Diling Liao, Liguo Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hyperspectral image classification deep feature extraction inverted residual convolution network global 3D attention Science Q
Acceso en línea:	https://doaj.org/article/bb856118ed184d3b857026df1cefcc80
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spelling	oai:doaj.org-article:bb856118ed184d3b857026df1cefcc802021-11-11T18:58:30ZDeep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification10.3390/rs132144722072-4292https://doaj.org/article/bb856118ed184d3b857026df1cefcc802021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4472https://doaj.org/toc/2072-4292Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.Tianyu ZhangCuiping ShiDiling LiaoLiguo WangMDPI AGarticlehyperspectral imageclassificationdeep feature extractioninverted residual convolution networkglobal 3D attentionScienceQENRemote Sensing, Vol 13, Iss 4472, p 4472 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hyperspectral image classification deep feature extraction inverted residual convolution network global 3D attention Science Q
spellingShingle	hyperspectral image classification deep feature extraction inverted residual convolution network global 3D attention Science Q Tianyu Zhang Cuiping Shi Diling Liao Liguo Wang Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
description	Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.
format	article
author	Tianyu Zhang Cuiping Shi Diling Liao Liguo Wang
author_facet	Tianyu Zhang Cuiping Shi Diling Liao Liguo Wang
author_sort	Tianyu Zhang
title	Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
title_short	Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
title_full	Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
title_fullStr	Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
title_full_unstemmed	Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
title_sort	deep spectral spatial inverted residual network for hyperspectral image classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bb856118ed184d3b857026df1cefcc80
work_keys_str_mv	AT tianyuzhang deepspectralspatialinvertedresidualnetworkforhyperspectralimageclassification AT cuipingshi deepspectralspatialinvertedresidualnetworkforhyperspectralimageclassification AT dilingliao deepspectralspatialinvertedresidualnetworkforhyperspectralimageclassification AT liguowang deepspectralspatialinvertedresidualnetworkforhyperspectralimageclassification
_version_	1718431668551811072

Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

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