  Existing deep learning based methods effectively prompt the performance of
aerial scene classification. However, due to the large amount of parameters and
computational cost, it is rather difficult to apply these methods to multiple
real-time remote sensing applications such as on-board data preception on
drones and satellites. In this paper, we address this task by developing a
light-weight ConvNet named multi-stage duplex fusion network (MSDF-Net). The
key idea is to use parameters as little as possible while obtaining as strong
as possible scene representation capability. To this end, a residual-dense
duplex fusion strategy is developed to enhance the feature propagation while
re-using parameters as much as possible, and is realized by our duplex fusion
block (DFblock). Specifically, our MSDF-Net consists of multi-stage structures
with DFblock. Moreover, duplex semantic aggregation (DSA) module is developed
to mine the remote sensing scene information from extracted convolutional
features, which also contains two parallel branches for semantic description.
Extensive experiments are conducted on three widely-used aerial scene
classification benchmarks, and reflect that our MSDF-Net can achieve a
competitive performance against the recent state-of-art while reducing up to
80% parameter numbers. Particularly, an accuracy of 92.96% is achieved on AID
with only 0.49M parameters.
