부산대학교 도서관

Remote sensing image scene classification (RSISC) as an interesting research direction in the field of remote sensing image interpretation for its broad applications. Recently, the non-local attention mechanism has been demonstrated effectiveness in computer vision and remote sensing image understanding. However, most non-local attention-based methods use a further projection on the same feature map can only bring limited performance gain. In addition, they are usually suffered from expensive computation costs. To solve the above problems, we present a novel progressive non-local aggregation network (PNLANet), which can progressively aggregate long-range feature dependencies from multi-scale feature maps effectively and efficiently. Firstly, a lightweight cross-layer non-local (L-CL-NL) module is designed to compute the long-range dependencies between different convolutional feature maps. Then, this module is recurrently utilized to progressively acquire global contextual scene representation from multi-scale feature maps. The effectiveness of PNLANet is validated on two challenging benchmarks, i.e., AID and NWPU-RESISC45. The results show that it can obtain richer global understanding ability with cheaper computation costs as well as improve classification performance.