MobileShuffle: An Efficient CNN Architecture for Spaceborne SAR Scene Classification

Currently, with the increasing number of satellites and the expansion of applications, the amount of remote sensing image data is growing geometrically. Due to limitations, such as bandwidth and power, it is less efficient to transmit the large amounts of data to ground servers for further processing. Real-time onboard image inference is clearly a better choice. However, previous efficient neural networks are mostly optimized for parameters rather than inference speed. To alleviate the speed bottleneck, an efficient backbone MobileShuffle combining hardware-friendly modules and structural reparameterization technique is proposed in this letter. In the proposed network, large amounts of depthwise separable convolution (DSC) are used to accelerate the inference process. A new structural reparameterization method is applied to enable the network better focus on the critical part of SAR images. Visualized heatmaps show that the learning focus of the network is optimized by multibranch architecture during training. The smallest variant has similar latency to the state-of-the-art resource efficient network MobileOne-S0, but with a 0.9% improvement in accuracy. Results on optical and SAR datasets show that MobileShuffle has excellent accuracy-speed tradeoff compared with other networks. Code and models are available at https://github.com/2474137474/MobileShuffle.