Design of a High-Efficiency Temporal Engine for Real-Time Spatial Satellite Image Classification Using Augmented Incremental Transfer Learning for Crop Analysis-Reference-Cited by-同舟云学术

Design of a High-Efficiency Temporal Engine for Real-Time Spatial Satellite Image Classification Using Augmented Incremental Transfer Learning for Crop Analysis

Published:2024-05-27 Issue:5 Volume:5 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Dhande Akshay Pramodrao,Malik Rahul,Saini Dipen,Garg Rachit,Jha Sudan,Nazeer Jabeen,Ahmad Sultan^ORCID

Funder

Prince Sattam bin Abdulaziz University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-024-02939-6.pdf

Reference34 articles.

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2. Shelestov A, et al. Cloud approach to automated crop classification using sentinel-1 imagery. IEEE Trans Big Data. 2020;6(3):572–82. https://doi.org/10.1109/TBDATA.2019.2940237.

3. Arri HS, Ramandeep S, Sudan J, Deepak P, Gyanendra PJ, Ill CD. Optimized task group aggregation-based overflow handling on fog computing environment using neural computing. Mathematics. 2021;9(19):2522. https://doi.org/10.3390/math9192522.

4. Jiang J, et al. HISTIF: a new spatiotemporal image fusion method for high-resolution monitoring of crops at the subfield level. IEEE J Sel Top Appl Earth Observ Remote Sens. 2020;13:4607–26. https://doi.org/10.1109/JSTARS.2020.3016135.

5. Li Z, Chen G, Zhang T. A CNN-transformer hybrid approach for crop classification using multitemporal multisensor images. IEEE J Sel Top Appl Earth Observ Remote Sens. 2020;13:847–58. https://doi.org/10.1109/JSTARS.2020.2971763.

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