A semantic features-enhanced dispensation network for retrieving remote sensing images-Reference-Cited by-同舟云学术

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A semantic features-enhanced dispensation network for retrieving remote sensing images

Published:2024-06-13 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Unar Salahuddin^ORCID,Unar Saifullah,Su Yining,Liu Pengbo,Fu Xianping

Funder

National Natural Science Foundation of China

Research Project of China Disabled Persons’ Federation on Assistive Technology

Liaoning Revitalization Talents Program

Dalian Science and Technology Innovation Fund

Qilu Institute of Technology

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13042-024-02239-8.pdf

Reference52 articles.

1. Zhang Z, Lu L, Zhao Y et al (2023) Recent advances in using Chinese Earth observation satellites for remote sensing of vegetation. ISPRS J Photogramm Remote Sens 195:393–407. https://doi.org/10.1016/J.ISPRSJPRS.2022.12.006

2. Lv N, Zhang Z, Li C et al (2023) A hybrid-attention semantic segmentation network for remote sensing interpretation in land-use surveillance. Int J Mach Learn Cybern 14:395–406. https://doi.org/10.1007/s13042-022-01517-7

3. Zheng K, Gao L, Ran Q et al (2019) Separable-spectral convolution and inception network for hyperspectral image super-resolution. Int J Mach Learn Cybern 10:2593–2607. https://doi.org/10.1007/s13042-018-00911-4

4. Wang S, Hou D, Xing H (2022) A novel multi-attention fusion network with dilated convolution and label smoothing for remote sensing image retrieval. Int J Remote Sens 43:1306–1322. https://doi.org/10.1080/01431161.2022.2035465

5. Liu GH, Yang JY (2023) Exploiting deep textures for image retrieval. Int J Mach Learn Cybern 14:483–494. https://doi.org/10.1007/s13042-022-01645-0

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