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Integration of Polarimetric Signature-Based Features and Texture Analysis for Accurate Soil Salinity Classification with Sentinel-1 | ||
| Earth Observation and Geomatics Engineering | ||
| دوره 9، شماره 2، اسفند 2025 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2026.409996.1211 | ||
| نویسندگان | ||
| Aryadokht Nouri1؛ Tayabe Managhebi* 2 | ||
| 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran - | ||
| چکیده | ||
| Soil salinity is a major driver of land degradation, and its reliable mapping remains challenging under frequent surface conditions. To address these limitations, this study presents a polarimetric based soil salinity classification framework that integrates dual polarimetric signature with texture information. In this context, an ascending Sentinel-1 dual-polarization (VV/VH) SLC scene acquired on 8 March 2024 was analyzed over the coastal districts of Khulna, Satkhira, and Jessore in southwest Bangladesh, which was deliberately selected to be temporally consistent with the field campaign conducted between 1–9 March 2024. First, texture measures were extracted from VV and VH intensity images using gray level co-occurrence matrix statistics in order to capture salinity related spatial pattern variations associated with surface roughness and crust formation. Next, dual polarimetric signatures were generated by synthesizing polarization states through linear combinations of the available channels, and a set of signature shape descriptors (e.g., pedestal height, extrema-related slopes, skewness, kurtosis, peak intensity, and signature width) was derived to enrich the feature space and improving class separability. Subsequently, to mitigate redundancy among heterogeneous predictors and enhance generalization, a feature selection step was applied to retain the most informative features for salinity discrimination. Finally, soil salinity was classified into three levels (low, moderate, and high) using two non-linear classifiers, support vector machine and multilayer perceptron, and performance was evaluated on an independent test set using confusion matrix metrics. Overall, the best-performing configuration achieved an overall accuracy of 91.6% and a Kappa coefficient of 0.861, thus demonstrating strong agreement with reference labels and underscoring the complementary value of texture and synthesized polarimetric response for dual-pol Sentinel-1 salinity mapping in coastal regions. | ||
| کلیدواژهها | ||
| Soil salinity mapping؛ Dual-polarization SAR؛ Polarimetric signature؛ Feature extraction؛ Sentinel-1 | ||
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آمار تعداد مشاهده مقاله: 262 |
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