Abstract
The high intra class diversity of remote sensing image scene often leads to the problem of difficult classification of remote sensing image scenes. Therefore, this paper proposes the CA-EfficientNetV2 model, embedding the coordinate attention into the head of the EfficientNetV2 network to enhance the classification effect. The coordinate attention is used to generate the position relationship between image spaces and channels so as to learn features efficiently. We trained three improved models CA-EfficientNetV2-S, CA-EfficientNetV2-M and CA-EfficientNetV2-L on UC Merced remote sensing dataset respectively. The classification accuracy reached 99.55%, 97.49% and 97.09% respectively. Among them, CA-EfficientNetV2-S had the best effect, which was improved by 0.8% compared with the original network.
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