期刊
IEEE ACCESS
卷 8, 期 -, 页码 6344-6357出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2963769
关键词
Remote sensing images; scene classification; spatial attention; channel attention; residual attention network
资金
- National Natural Science Foundation of China [41871226, 41571401]
- Special Foundation of Postdoctoral Scientific Research Project of Chongqing [Xm2016081]
Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly contain various types of ground objects and complex backgrounds, most of methods cannot focus on saliency features of scene, which limits the classification performances. To address this issue, we propose a novel Saliency Dual Attention Residual Network (SDAResNet) to extract both cross-channel and spatial saliency information for scene classification of RSI. More specifically, the proposed SDAResNet consists of spatial attention and channel attention, in which spatial attention is embedded in low-level feature to emphasize saliency location information and suppress background information, and channel attention is integrated to high-level features to extract saliency meaningful information. Additionally, several image classification tricks are used to further improve classification accuracy. Finally, Extensive experiments on two challenging benchmark RSI datasets are presented to demonstrate that our methods outperform most of state-of-the-art approaches significantly.
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