Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples

Lin Zhou; Jinbiao Zhu; Jihao Yang; Jie Geng

doi:10.1109/ICUS55513.2022.9986968

Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples

Lin Zhou, Jinbiao Zhu, Jihao Yang, Jie Geng

School of Electronics and Information

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Hyperspectral image classification is a prominent topic in many remote sensing applications, but the limited number of manually annotated samples leads to performance bottlenecks. To resolve this issue, a data augmentation and spatial-spectral residual framework is proposed for hyperspectral image classification using limited samples. Firstly, an unsupervised pseudo-sample generation method is proposed to augment the sample set, and the generalization capability of the model is improved by mixup operations. Then, to adequately extract the spatial-spectral features of hyperspectral images, a spatial-spectral residual framework is designed to improve the classification performance of the model. The qualitative and quantitative experiments were carried out on Indian Pines dataset to validate the effectiveness of the model.

Original language	English
Title of host publication	Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Editors	Rong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	490-495
Number of pages	6
ISBN (Electronic)	9781665484565
DOIs	https://doi.org/10.1109/ICUS55513.2022.9986968
State	Published - 2022
Event	2022 IEEE International Conference on Unmanned Systems, ICUS 2022 - Guangzhou, China Duration: 28 Oct 2022 → 30 Oct 2022

Publication series

Name	Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

Conference

Conference	2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Country/Territory	China
City	Guangzhou
Period	28/10/22 → 30/10/22

Keywords

data augmentation
deep learning
few sample classification
hyperspectral image

Access to Document

10.1109/ICUS55513.2022.9986968

Cite this

Zhou, L., Zhu, J., Yang, J., & Geng, J. (2022). Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples. In R. Song (Ed.), Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022 (pp. 490-495). (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS55513.2022.9986968

Zhou, Lin ; Zhu, Jinbiao ; Yang, Jihao et al. / Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples. Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. editor / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 490-495 (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022).

@inproceedings{3b48e7313b9d4428b94de4ad31c5eb41,

title = "Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples",

abstract = "Hyperspectral image classification is a prominent topic in many remote sensing applications, but the limited number of manually annotated samples leads to performance bottlenecks. To resolve this issue, a data augmentation and spatial-spectral residual framework is proposed for hyperspectral image classification using limited samples. Firstly, an unsupervised pseudo-sample generation method is proposed to augment the sample set, and the generalization capability of the model is improved by mixup operations. Then, to adequately extract the spatial-spectral features of hyperspectral images, a spatial-spectral residual framework is designed to improve the classification performance of the model. The qualitative and quantitative experiments were carried out on Indian Pines dataset to validate the effectiveness of the model.",

keywords = "data augmentation, deep learning, few sample classification, hyperspectral image",

author = "Lin Zhou and Jinbiao Zhu and Jihao Yang and Jie Geng",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Unmanned Systems, ICUS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",

year = "2022",

doi = "10.1109/ICUS55513.2022.9986968",

language = "英语",

series = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "490--495",

editor = "Rong Song",

booktitle = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",

}

Zhou, L, Zhu, J, Yang, J & Geng, J 2022, Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples. in R Song (ed.), Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 490-495, 2022 IEEE International Conference on Unmanned Systems, ICUS 2022, Guangzhou, China, 28/10/22. https://doi.org/10.1109/ICUS55513.2022.9986968

Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples. / Zhou, Lin; Zhu, Jinbiao; Yang, Jihao et al.
Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. ed. / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2022. p. 490-495 (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples

AU - Zhou, Lin

AU - Zhu, Jinbiao

AU - Yang, Jihao

AU - Geng, Jie

PY - 2022

Y1 - 2022

N2 - Hyperspectral image classification is a prominent topic in many remote sensing applications, but the limited number of manually annotated samples leads to performance bottlenecks. To resolve this issue, a data augmentation and spatial-spectral residual framework is proposed for hyperspectral image classification using limited samples. Firstly, an unsupervised pseudo-sample generation method is proposed to augment the sample set, and the generalization capability of the model is improved by mixup operations. Then, to adequately extract the spatial-spectral features of hyperspectral images, a spatial-spectral residual framework is designed to improve the classification performance of the model. The qualitative and quantitative experiments were carried out on Indian Pines dataset to validate the effectiveness of the model.

AB - Hyperspectral image classification is a prominent topic in many remote sensing applications, but the limited number of manually annotated samples leads to performance bottlenecks. To resolve this issue, a data augmentation and spatial-spectral residual framework is proposed for hyperspectral image classification using limited samples. Firstly, an unsupervised pseudo-sample generation method is proposed to augment the sample set, and the generalization capability of the model is improved by mixup operations. Then, to adequately extract the spatial-spectral features of hyperspectral images, a spatial-spectral residual framework is designed to improve the classification performance of the model. The qualitative and quantitative experiments were carried out on Indian Pines dataset to validate the effectiveness of the model.

KW - data augmentation

KW - deep learning

KW - few sample classification

KW - hyperspectral image

UR - http://www.scopus.com/inward/record.url?scp=85146492459&partnerID=8YFLogxK

U2 - 10.1109/ICUS55513.2022.9986968

DO - 10.1109/ICUS55513.2022.9986968

M3 - 会议稿件

AN - SCOPUS:85146492459

T3 - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

SP - 490

EP - 495

BT - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

A2 - Song, Rong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

Y2 - 28 October 2022 through 30 October 2022

ER -

Zhou L, Zhu J, Yang J, Geng J. Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples. In Song R, editor, Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 490-495. (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022). doi: 10.1109/ICUS55513.2022.9986968

Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this