Classification of very high-resolution (VHR) aerial remote sensing (RS)
images is a well-established research area in the remote sensing community as
it provides valuable spatial information for decision-making. Existing works on
VHR aerial RS image classification produce an excellent classification
performance; nevertheless, they have a limited capability to well-represent VHR
RS images having complex and small objects, thereby leading to performance
instability. As such, we propose a novel plug-and-play multi-scale attention
feature extraction block (MSAFEB) based on multi-scale convolution at two
levels with skip connection, producing discriminative/salient information at a
deeper/finer level. The experimental study on two benchmark VHR aerial RS image
datasets (AID and NWPU) demonstrates that our proposal achieves a
stable/consistent performance (minimum standard deviation of $0.002$) and
competent overall classification performance (AID: 95.85\% and NWPU: 94.09\%).

该研究论文通过多尺度注意力特征提取块（MSAFEB）基于多尺度卷积和跳跃连接，实现了对复杂和小目标的高分辨率航空遥感图像的稳定分类性能。在两个基准数据集上的实验研究表明，该方法达到了稳定的性能（最小标准差为 0.002）和良好的整体分类性能（AID：95.85％和 NWPU：94.09％）。