An Unsupervised Clustering Technique for Dual-Pol Sentinel-1 SLC and GRD SAR Data

Synthetic aperture radar (SAR) data classification has gained significant research interest, as accurate land-cover information is vital in a wide range of planning and management activities. While classification algorithms for full-polarimetric (full-pol) SAR data are typically based on the statistical or physical characteristics of the scattering mechanism from targets, classification of co-cross polarization (VV-VH or HH-HV) dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity information due to its limited polarimetric information. Several studies also employ the dual-pol entropy/alpha decomposition parameters, establishing a conventional framework for supervised and unsupervised classification of dual-pol SAR data. However, it is essential to note that the conventional approach cannot differentiate between certain elementary targets, leading to misclassification among diverse land-cover targets. To address this limitation, we introduce an unsupervised clustering technique for dual-pol Sentinel-1 SAR data utilizing the conventional entropy parameter alongside a dual-pol target characteristic parameter that discriminates between various land-cover targets, including “dihedral-like” (buildings, etc.) and “surface-like” (water bodies, etc.) targets in a dual-pol scene. Thus, the proposed clustering scheme, which applies to both single look complex (SLC) and ground range detected (GRD) SAR, categorizes it into eight clusters, each representing specific target characteristics. We adopted two strategies to assess the proposed clustering scheme: 1) cluster zones obtained for diverse land-cover targets spanning continents and 2) temporal changes in cluster zones over rice-cultivated fields at various growth stages. The proposed approach effectively discriminates diverse land-cover targets and distinct growth stages of rice.