LAUSR

Remote sensing image scene classification plays a significant role in remote sensing image analysis. Aiming at the problems of large transformation and scale variation of background and key objects in remote sensing images, we propose a neural architecture search method based on attention search space. The network adaptively searches convolution, pooling and attention operations in the appropriate layers. In order to ensure the stability of the searching process, a multi-stage network progressive fusion search method is proposed, which discards useless operations in stages, reduces the burden of search algorithm and improves the search efficiency. Finally, paying attention to the association information between objects and scenes, a bottom-up multi-scale fusion network connection strategy is proposed to fully reuse the semantics of multi-scale feature maps in each stage. The experimental results show that the proposed method performs better than the manual method and the current neural network architecture search method.