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.

翻译：甚高分辨率遥感场景分类因较高的类间相似性和类内变异性问题而具有挑战性。近年来，基于深度学习的现有方法在甚高分辨率遥感场景分类中展现出巨大潜力，但仍存在分类性能不稳定的问题。为解决这一问题，本文提出一种新颖的深度学习方法。为此，我们设计了一个增强型甚高分辨率注意力模块（EAM），随后结合空洞空间金字塔池化（ASPP）与全局平均池化（GAP）。该过程从相应层级提取增强特征，然后进行多层次特征融合。在两个广泛使用的甚高分辨率遥感数据集上的实验结果表明，所提方法在取得具有竞争力且稳定/鲁棒的分类性能的同时，其标准差最低可达0.001。此外，在AID和NWPU数据集上分别取得了95.39%和93.04%的最高总体精度。