Evaluation of U-Net Variants and Traditional Machine Learning Methods for Land Cover Classification Using High-Resolution Satellite Imagery

Ebrahimi, Aydin; Ghourkhanehchi Zirak, Amirhossein; Sedaghat, Amin; Mohammadi, Nazila

doi:10.5194/isprs-annals-X-4-W8-2025-205-2026

Articles | Volume X-4/W8-2025

https://doi.org/10.5194/isprs-annals-X-4-W8-2025-205-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/isprs-annals-X-4-W8-2025-205-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume X-4/W8-2025

29 May 2026

| 29 May 2026

Evaluation of U-Net Variants and Traditional Machine Learning Methods for Land Cover Classification Using High-Resolution Satellite Imagery

Aydin Ebrahimi, Amirhossein Ghourkhanehchi Zirak, Amin Sedaghat, and Nazila Mohammadi

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PERFORMANCE EVALUATION OF LEARNING-BASED METHODS FOR MULTISPECTRAL SATELLITE IMAGE MATCHING

N. Jovhari, N. Farhadi, A. Sedaghat, and N. Mohammadi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 335–341, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-335-2023,https://doi.org/10.5194/isprs-annals-X-4-W1-2022-335-2023, 2023

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