Dynamic Hybrid Knowledge Distillation for Fine-Grained Aircraft Classification in Low-Resolution Remote Sensing Images

Fine-grained aircraft classification in optical remote sensing images relies on subtle difference of local parts. However, aircrafts in low-resolution (LR) images are depicted as small objects with blurred details and limited appearance information, resulting in the dramatic classification performance drop. We propose a novel dynamic hybrid knowledge distillation (DHKD) method to enhance fine-grained aircraft classification in LR remote sensing images. The core idea is to construct a teacher network for high-resolution (HR) images and a student network for LR images, and dynamically distill the key discriminative region attention-guided feature-based and logit-based object prior knowledge acquired from the teacher to the student to instruct its training, thereby improving the student’s fine-grained representation capability and classification performance for LR images. The distillation weights of different knowledge types are adaptive to their optimization direction similarity with that of the primary fine-grained classification task during the iterations. Extensive experiments on the MAR20 dataset demonstrate that DHKD consistently outperforms state-of-the-art distillation methods on fine-grained classification for aircrafts with absolute sizes of 32, 20, and 12 pixels in LR remote sensing images.