Research Article

Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring

Volume: 8 October 5, 2026

Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring

Abstract

This study evaluates the robustness of a lightweight deep learning framework for Synthetic Aperture Radar (SAR) land-cover classification under simulated airborne radar imaging degradations. A MobileNetV3-CBAM model was trained on 8,000 SAR image patches representing four land-cover classes: grassland, bareland, agriculture, and urban land. To simulate controlled variations in SAR image quality associated with airborne radar acquisition conditions, degradations related to speckle noise and spatial resolution were applied to the test data. A degradation-aware fine-tuning strategy was then introduced to improve model robustness under these challenging conditions. The baseline MobileNetV3-CBAM model achieved a macro-F1 score of 0.9409 on clean SAR images. After degradation-aware fine-tuning, the macro-F1 score increased to 0.9624 on clean images and showed substantial improvements under degraded conditions. In particular, the macro-F1 score improved from 0.2808 to 0.9413 under severe spatial resolution degradation and from 0.5069 to 0.9261 under severe speckle noise degradation. Furthermore, the Robustness Index (RI) increased from 0.5436 for the baseline model to 0.9854 after fine-tuning, indicating a marked enhancement in the model’s ability to maintain classification performance under degraded imaging conditions. A comparison with the baseline MobileNetV3 architecture was also conducted to examine whether the CBAM attention mechanism provided additional benefits under the evaluated degradation-aware conditions. The final MobileNetV3-CBAM model contains 1.27 million trainable parameters and has a model size of 4.96 MB, making it suitable for lightweight SAR image classification applications. These results demonstrate that degradation-aware fine-tuning substantially enhances the robustness of SAR land-cover classification and provides a practical framework for evaluating model sensitivity to controlled SAR image-quality degradations relevant to airborne pastoral ecosystem monitoring.

Keywords

Supporting Institution

SSL Laboratory

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Photogrammetry and Remote Sensing

Journal Section

Research Article

Publication Date

October 5, 2026

Submission Date

July 17, 2026

Acceptance Date

September 18, 2026

Published in Issue

Year 2026 Volume: 8

APA
Benouakta, N., Slimane, Z., & Benadda, B. (2026). Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring. Turkish Journal of Remote Sensing, 8. https://doi.org/10.51489/tuzal.1996893
AMA
1.Benouakta N, Slimane Z, Benadda B. Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring. TJRS. 2026;8. doi:10.51489/tuzal.1996893
Chicago
Benouakta, Narimane, Zohra Slimane, and Belkacem Benadda. 2026. “Development of a Lightweight Degradation-Aware Framework for SAR Land-Cover Classification and Pastoral Ecosystem Monitoring”. Turkish Journal of Remote Sensing 8 (October). https://doi.org/10.51489/tuzal.1996893.
EndNote
Benouakta N, Slimane Z, Benadda B (October 1, 2026) Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring. Turkish Journal of Remote Sensing 8
IEEE
[1]N. Benouakta, Z. Slimane, and B. Benadda, “Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring”, TJRS, vol. 8, Oct. 2026, doi: 10.51489/tuzal.1996893.
ISNAD
Benouakta, Narimane - Slimane, Zohra - Benadda, Belkacem. “Development of a Lightweight Degradation-Aware Framework for SAR Land-Cover Classification and Pastoral Ecosystem Monitoring”. Turkish Journal of Remote Sensing 8 (October 1, 2026). https://doi.org/10.51489/tuzal.1996893.
JAMA
1.Benouakta N, Slimane Z, Benadda B. Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring. TJRS. 2026;8. doi:10.51489/tuzal.1996893.
MLA
Benouakta, Narimane, et al. “Development of a Lightweight Degradation-Aware Framework for SAR Land-Cover Classification and Pastoral Ecosystem Monitoring”. Turkish Journal of Remote Sensing, vol. 8, Oct. 2026, doi:10.51489/tuzal.1996893.
Vancouver
1.Narimane Benouakta, Zohra Slimane, Belkacem Benadda. Development of a lightweight degradation-aware framework for SAR land-cover classification and pastoral ecosystem monitoring. TJRS. 2026 Oct. 1;8. doi:10.51489/tuzal.1996893

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