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Existing deep learning based methods effectively prompt the performance ofaerial scene classification. However, due to the large amount of parameters andcomputational cost, it is rather difficult to apply these methods to multiplereal-time remote sensing applications such as on-board data preception ondrones and satellites. In this paper, we address this task by developing alight-weight ConvNet named multi-stage duplex fusion network (MSDF-Net). Thekey idea is to use parameters as little as possible while obtaining as strongas possible scene representation capability. To this end, a residual-denseduplex fusion strategy is developed to enhance the feature propagation whilere-using parameters as much as possible, and is realized by our duplex fusionblock (DFblock). Specifically, our MSDF-Net consists of multi-stage structureswith DFblock. Moreover, duplex semantic aggregation (DSA) module is developedto mine the remote sensing scene information from extracted convolutionalfeatures, which also contains two parallel branches for semantic description.Extensive experiments are conducted on three widely-used aerial sceneclassification benchmarks, and reflect that our MSDF-Net can achieve acompetitive performance against the recent state-of-art while reducing up to80% parameter numbers. Particularly, an accuracy of 92.96% is achieved on AIDwith only 0.49M parameters.

A Multi-Stage Duplex Fusion ConvNet for Aerial Scene Classification