Surface Scattering Classification Using Dual‑Polarization ALOS PALSAR Data in the Zagros Fold–Thrust Belt, SW Iran

Ameri, Sima; Najafi, SeyedAbbas

doi:10.48306/jgrs.2026.530660.1014

Surface Scattering Classification Using Dual‑Polarization ALOS PALSAR Data in the Zagros Fold–Thrust Belt, SW Iran

نوع مقاله : Original Article

نویسندگان

Sima Ameri ¹

SeyedAbbas Najafi ²

¹ Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahwaz, Ahwaz, Iran

² Department of Petroleum Engineering, Abadan Faculty of Petroleum, Petroleum University of Technology, Abadan, Iran

10.48306/jgrs.2026.530660.1014

چکیده

This study presents a framework for identifying terrain-related surface scattering regimes using dual-polarization ALOS PALSAR (L-band) data over a 4,852 km² area in the central Zagros Fold–Thrust Belt, SW Iran. The methodology integrates three complementary radar-derived features: (1) a surface roughness proxy computed from spatial gradients of HH backscatter intensity, (2) the Polarization Ratio (PR), and (3) a modified Radar Vegetation Index (RVI). Following radiometric preprocessing and speckle reduction, features were log-transformed, standardized, and reduced using Principal Component Analysis (PCA). K-means clustering as a unsupervised classification was applied to the standardized SAR-derived features in order to identify five dominant scattering classes across the study area. Class-wise roughness distributions exhibit marked contrasts, ranging from highly homogeneous and low-roughness conditions to strongly heterogeneous and high-roughness domains. One-way ANOVA results indicate very strong statistical separation among classes (p < 0.001, η² = 0.801), signifying that over 80% of the total roughness variance is explained by class membership. High-roughness classes account for approximately 26.6% of the mapped area, and global roughness percentiles reveal a pronounced right-tailed distribution consistent with heterogeneous structural conditions. These results demonstrate that intensity-based roughness metrics combined with dual-polarization SAR indicators effectively capture spatial variability in radar scattering behavior. The framework is computationally efficient and objective, suitable for large regions. Although the resulting classes represent statistical scattering regimes rather than explicit geomorphological categories, the strong internal separability and spatial coherence of the clusters highlight the capability of L-band SAR to capture regional-scale surface heterogeneity. This approach offers a practical framework for unsupervised terrain analysis in structurally complex environments

کلیدواژه‌ها

SAR Surface Roughness, Dual-Polarization SAR, Unsupervised Clustering, ALOS PALSAR, Zagros Fold&ndash

Thrust Belt

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