A Lightweight Mining-Area Remote Sensing Scene Classification Framework via Knowledge Distillation and Channel-Aware Non-Local Attention

Mining-area remote sensing scene classification plays an important role in mineral resource monitoring and ecological environment assessment. However, high-accuracy deep learning models usually exhibit complex architectures, large parameter sizes, and high computational costs, which limit their deployment on resource-constrained edge devices. To address this issue, this paper proposes a knowledge distillation and channel-aware non-local attention network (KDANet) for lightweight mining-area remote sensing scene classification. Specifically, a pretrained multi-layer Transformer-based high-performance network (HTransNet) is adopted as the teacher model, while ShuffleNetV2 is used as the lightweight student model. The proposed channel-aware non-local attention module (CANLAM) adaptively enhances high-level features during the distillation process, enabling the student model to better capture critical channel-wise and spatial contextual information. Furthermore, a multi-perspective distillation strategy is constructed by jointly optimizing contrastive distillation loss, relational distillation loss, and feature distillation loss, guiding the student model to approximate the teacher model from both feature representation and output distribution perspectives. Experimental results on the previously constructed CUG_MA mining scene dataset demonstrate that, with a reduction of approximately 52% in parameters and 62% in computational cost, KDANet achieves an overall accuracy (OA) of 69.48%. The lightweight student model maintains competitive classification performance while significantly improving computational efficiency and outperforming several mainstream distillation approaches.