RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework

Yuelei Wang; Ting Zhang; Liangjin Zhao; Lin Hu; Zhechao Wang; Ziqing Niu; Peirui Cheng; Kaiqiang Chen; Xuan Zeng; Zhirui Wang; Hongqi Wang; Xian Sun

arXiv · 2309.09003

RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework

Yuelei Wang·Ting Zhang·Liangjin Zhao·Lin Hu·Zhechao Wang·Ziqing Niu·Peirui Cheng·Kaiqiang Chen·Xuan Zeng·Zhirui Wang·Hongqi Wang·Xian Sun

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Abstract

In recent years, remote sensing (RS) vision foundation models such as RingMo have emerged and achieved excellent performance in various downstream tasks. However, the high demand for computing resources limits the application of these models on edge devices. It is necessary to design a more lightweight foundation model to support on-orbit RS image interpretation. Existing methods face challenges in achieving lightweight solutions while retaining generalization in RS image interpretation. This is due to the complex high and low-frequency spectral components in RS images, which make traditional single CNN or Vision Transformer methods unsuitable for the task. Therefore, this paper proposes RingMo-lite, an RS multi-task lightweight network with a CNN-Transformer hybrid framework, which effectively exploits the frequency-domain properties of RS to optimize the interpretation process. It is combined by the Transformer module as a low-pass filter to extract global features of RS images through a dual-branch structure, and the CNN module as a stacked high-pass filter to extract fine-grained details effectively. Furthermore, in the pretraining stage, the designed frequency-domain masked image modeling (FD-MIM) combines each image patch's high-frequency and low-frequency characteristics, effectively capturing the latent feature representation in RS data. As shown in Fig. 1, compared with RingMo, the proposed RingMo-lite reduces the parameters over 60% in various RS image interpretation tasks, the average accuracy drops by less than 2% in most of the scenes and achieves SOTA performance compared to models of the similar size. In addition, our work will be integrated into the MindSpore computing platform in the near future.

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BibTeX RIS

Yuelei Wang, Ting Zhang, Liangjin Zhao, Lin Hu, Zhechao Wang, Ziqing Niu, Peirui Cheng, Kaiqiang Chen, Xuan Zeng, Zhirui Wang, Hongqi Wang, Xian Sun. 2023-09-16. RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework. https://arxiv.org/abs/2309.09003

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