Superpixelwise PCA based data augmentation for hyperspectral image classification-Reference-Cited by-同舟云学术

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Superpixelwise PCA based data augmentation for hyperspectral image classification

Published:2024-03-09 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Gao Shang,Jiang Xinwei^ORCID,Zhang Yongshan,Liu Xiaobo,Xiong Qianjin,Cai Zhihua

Funder

Nature Science Foundation of Hubei Province

National Natural Science Foundation of China

Research on Natural Resources Monitoring System based on Multi-source Remote Sensing Data

Fundamental Research Founds for National University, China University of Geosciences

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18667-1.pdf

Reference34 articles.

1. Acción Á, Argüello F, Heras DB (2020) Dual-window superpixel data augmentation for hyperspectral image classification. Appl Sci 10:8833. https://doi.org/10.3390/app10248833

2. Alafandy K, Omara H, LAZAAR M, et al (2020) Investment of classic deep cnns and svm for classifying remote sensing images. Adv Sci Technol Eng Syst J 05:652–659. https://doi.org/10.25046/aj050580

3. Alafandy K, Omara H, LAZAAR M, et al (2020) Using classic networks for classifying remote sensing images: Comparative study. Adv Sci Technol Eng Syst J 05:770–780. https://doi.org/10.25046/aj050594

4. Alafandy KA, Omara H, El-Sayed HS et al (2022) Efficient classification of remote sensing images using two convolution channels and svm. Comput Mater Continua 72(1):739–753. https://doi.org/10.32604/cmc.2022.022457

5. Chen Y, Jiang H, Li C et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

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