An Evidence Modified Gaussian Process Classifier (EM-GPC) for Crop Classification Using Dual-Polarimetric C- and L- Band SAR Data

Accurate crop classification with synthetic aperture radar (SAR) data is a significant area of research and translating into practice from local to regional scale crop inventory mapping. With the growing accessibility to abundant data sources from both current and upcoming dual-polarimetric SAR missions, the capability to generate precise crop maps is set to enhance substantially. The geometric and dielectric properties of targets highly influence radar backscatter. Especially for agricultural crops, which exhibit dynamic changes in target properties and physiological structure throughout their phenology, discriminating between crops using SAR data remains a significant challenge. This study introduces a novel Gaussian process classifier model called the evidence modified Gaussian process classifier (EM-GPC) that employs a regression approach for multiclass crop classification with a modified evidence. We utilize dual-polarimetric SAR data acquired over two study sites to perform crop classification utilizing EM-GPC. At first, we perform a phenology-based single-date crop classification using ESAR C- and L-band SAR data acquired over the DEMMIN site in Germany. The performance evaluation of the EM-GPC model revealed robust accuracy during various crop phenological stages, showcasing its adaptability to temporal variations. Further, we perform a multidate crop classification using C-band RADARSAT-2 and L-band simulated NISAR product from UAVSAR data acquired over Manitoba, Canada. In this study, EM-GPC successfully classifies major crop types (wheat, canola, soybeans, corn, barley, oats, and rye). The efficacy of the proposed model establishes its capability for crop classification utilizing dual-polarimetric SAR data in operational settings.