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- W3114860340 abstract "Scene classification plays a crucial role in understanding high-resolution remote sensing images. In this paper, we introduce a novel scene classification method based on deep attention network. It aims to integrate the attention mechanism and Convolutional Neural Network (CNN) to learn the more discriminative features from the scene content. Concretely we use three branches (main, spatial attention and channel attention branches) to explore the scene features. The main branch is the feature maps of the backbone CNN which are used to describe the high-level semantic features of scene. The spatial attention branch aims to investigate the long-range contextual dependencies through non-local operation. The channel attention branch intends to exploit the key semantic response. Finally, the three branches are fused to extract the more representative features and suppress irrelevant features. Experimental results on AID and NWPU-RESISC45 remote sensing scene datasets demonstrate the effectiveness of our method." @default.
- W3114860340 created "2021-01-05" @default.
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- W3114860340 date "2020-01-01" @default.
- W3114860340 modified "2023-09-26" @default.
- W3114860340 title "Deep Attention Network for Remote Sensing Scene Classification" @default.
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- W3114860340 doi "https://doi.org/10.1007/978-981-33-6033-4_21" @default.
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