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Very high-resolution (VHR) remote sensing (RS) scene classification is achallenging task due to the higher inter-class similarity and intra-classvariability problems. Recently, the existing deep learning (DL)-based methodshave shown great promise in VHR RS scene classification. However, they stillprovide an unstable classification performance. To address such a problem, we,in this letter, propose a novel DL-based approach. For this, we devise anenhanced VHR attention module (EAM), followed by the atrous spatial pyramidpooling (ASPP) and global average pooling (GAP). This procedure imparts theenhanced features from the corresponding level. Then, the multi-level featurefusion is performed. Experimental results on two widely-used VHR RS datasetsshow that the proposed approach yields a competitive and stable/robustclassification performance with the least standard deviation of 0.001. Further,the highest overall accuracies on the AID and the NWPU datasets are 95.39% and93.04%, respectively.

Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification