Very high-resolution (VHR) remote sensing (RS) scene classification is a
challenging task due to the higher inter-class similarity and intra-class
variability problems. Recently, the existing deep learning (DL)-based methods
have shown great promise in VHR RS scene classification. However, they still
provide an unstable classification performance. To address such a problem, we,
in this letter, propose a novel DL-based approach. For this, we devise an
enhanced VHR attention module (EAM), followed by the atrous spatial pyramid
pooling (ASPP) and global average pooling (GAP). This procedure imparts the
enhanced features from the corresponding level. Then, the multi-level feature
fusion is performed. Experimental results on two widely-used VHR RS datasets
show that the proposed approach yields a competitive and stable/robust
classification performance with the least standard deviation of 0.001. Further,
the highest overall accuracies on the AID and the NWPU datasets are 95.39% and
93.04%, respectively.

本文介绍了一种利用增强型 VHR 注意力模块、压缩空间金字塔和全局平均池化等技术的深度学习方法，对于经常出现的类别干扰问题，本文方法能够提供更加稳健的分类性能，可以用于非常高分辨率遥感场景分类任务。实验证明，该方法在两个常用的 VHR 遥感数据集上能够达到很好的分类效果。