Identification of saline landscapes from an integrated SVM approach from a novel 3-D classification schema using Sentinel-1 dual-polarized SAR data

This study presented an integrated SVM classification method that investigated the relationship between intrinsic scattering attributes of the surface features and the conductivity characteristics of the soil in the newly proposed two- and three-dimensional classification (2D and 3D) schema to classify the saline landscape. The study was conducted in the Vellore district, Tamil Nadu, which is composed of heterogeneous saline landforms due to the active geological processes and anthropogenic activities. The intensity products of Sentinel-1 data (VV + VH) of C-band frequency were employed in the present study. The SVM with linear kernel has outperformed the other models, namely random forest (RF), naive Bayes (NB), and K-Nearest Neighbour (k−NN), in performing the broad level of classification from the 2D classification schema (SVMOA = 84.2%, RFOA = 80.4%, k-NNOA = 78.8%, NBOA = 68.4%). The soil EC values derived from the dielectric loss measurements (R2 = 0.79, ρ = 0.018, and α=95%) were used to introduce the integrated SVM approach in the 3D schema to further breakdown the mapped classes into non-saline (NS) (soil EC ≤ 2 ds/cm), slightly saline (SS) (soil EC = 2.1 to 4 ds/cm) and moderately saline (MS) (soil EC = 4.1 to 8 ds/cm) categories. The overall performance of the integrated SVM approach implemented for the 3D classification schema (F1 = 0.80) was found to be satisfactory, but with an associated uncertainty majorly from MS (Precision = 0.52, F1 = 0.69), SS (Recall = 0.09, F1 = 0.15), and NS waterbodies (Recall = 0.18, F1 = 0.29) as shown in the Precision-Recall graph (AUCPR3D = 0.62). However, with the promising performance level demonstrated for the other nine classes such as NS, SS, and MS wet soil (F1 = 0.92, 0.92, 0.96), healthy plants (F1 = 0.83), salt-tolerant plants under SS and MS conditions (F1 = 0.83, 0.88), and waterlogged vegetation under NS, SS, and MS conditions (F1 = 0.82, 0.83, 0.83), the proposed classification scheme becomes an effective method to map saline and non-saline features from dual-polarimetric SAR data.