A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification

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\%).