RandommaskFormer: Light Weight Remote Sensing Scene Classification with Masked Transformer

Remote sensing scene classification aims to assign correct semantic labels to remote sensing images. Many state-of-the-art algorithms have made significant contributions to improving model accuracy. However, these algorithms often involve a large number of parameters and floating-point computations. To address this, we propose a lightweight network architecture named RandommaskFormer. This network initially utilizes random position filtering to reduce model complexity. Next, effective feature interactions are achieved through feature covariance analysis. Finally, we implement a two-stage optimization strategy, incorporating both label optimization and loss function optimization. Among the above strategy, we first perform clustering analysis on remote sensing image features using a KD tree to assign new visual labels. Then, we combine triplet loss with cross-entropy loss to guide model training. Experimental results on three mainstream datasets demonstrate the effectiveness of RandommaskFormer. Additionally, the deployment of the two-stage optimization strategy further improves the model's performance.