A bidirectional co-training teacher-student framework is developed to mitigate the performance degradation caused by the scarcity of labeled polarimetric synthetic aperture radar （PolSAR） runway detection data. Within this framework， a teaching assistant module is constructed to integrate distillation loss and feedback loss. Underutilized feature representations are identified through a systematic comparison of model inferences and the generation of directional feature vectors. Experimental results demonstrate that a detection accuracy of 83.11% is achieved by the proposed method on the UAVSAR dataset， with improvements of 15.63%， 6.46%， and 17.25% being observed over the Unet， D-Unet， and Unet++ baselines， respectively.