Data underlying the publication: RCNet: A Hybrid Framework for PolSAR Image Classification via Real-Complex Polarimetric Feature Fusion

Three benchmark PolSAR datasets were utilized to assess the performance of the proposed method in this study. Oberpfaffenhofen, San Francisco and Barnaul. The first dataset was acquired by the L-band PolSAR data from the E-SAR sensor, capturing the Oberpfaffenhofen area in Germany with a size of 1200 × 1300 pixels, and spatial resolution is 3 × 3 meters. The primary land cover types in this region include dense urban areas, forests, and some regions without artificial markings. The secondary dataset was collected via the airborne L-band PolSAR sensor aboard NASA/JPL's AIRSAR platform, covering the San Francisco region This dataset exhibits a spatial extent of 1024 × 900 pixels with 10-meter resolution, encompassing diverse land cover categories including urban zones, vegetated areas, rocky terrain, oceanic surfaces, and exposed landforms.The third dataset is from China’s Gaofen-3 satellitecollected on May 5, 2018. The multi-look data consists of 1474 × 1310 pixels. The observed area includes various land cover types such as urban areas, forests, farmland.These datasets are provided in this repository.