The Journal of China Universities of Posts and Telecommunications ›› 2020, Vol. 27 ›› Issue (6): 87-100.doi: 10.19682/j.cnki.1005-8885.2020.0048

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Parallel design of convolutional neural networks for remote sensing images object recognition based on data-driven array processor

Shan Rui, Jiang Lin, Deng Junyong, Cui Pengfei, Zhang Yuting, Wu Haoyue, Xie Xiaoyan   

  • Received:2019-06-24 Revised:2020-12-16 Online:2020-12-31 Published:2020-12-31
  • Contact: SHAN Rui
  • Supported by:
    Research on Self-reconfiguration Mechanism of Data Driven Light Core Array Processor;Performance-driven optical interconnect video array processor dynamic self-reconfiguration architecture;Key Technology of Programmable Dynamic Self-Reconfigurable 3D Array Chip Architecture;Research on Key Technologies of 3D Photoelectric Hybrid Network;Performance-Driven Programmable Self-Reconfigurable Graphics Processor Architecture;the Shaanxi Provincial Co-ordination Innovation Project of Science and Technology;the Shaanxi Provincial key R & D plan


Object recognition in very high-resolution remote sensing images is a basic problem in the field of aerial and
satellite image analysis. With the development of sensor technology and aerospace remote sensing technology, the quality and quantity of remote sensing images are improved. Traditional recognition methods have a certain limitation in describing higher-level features, but object recognition method based on convolutional neural network (CNN) can not only deal with large scale images, but also train features automatically with high efficiency. It is mainly used on object recognition for remote sensing images. In this paper, an AlexNet CNN model is trained using 2 100 remote sensing images, and correction rate can reach 97.6% after 2 000 iterations. Then based on trained model, a parallel design of CNN for remote sensing images object recognition based on data-driven array processor (DDAP) is proposed. The consuming cycles are counted. Simultaneously, the proposed architecture is realized on Xilinx V6 development board, and synthesized based on SMIC 130 nm complementary metal oxid semiconductor (CMOS) technology. The experimental results show that the proposed architecture has a certain degree of parallelism to achieve the purpose of accelerating calculations.

Key words: convolutional neural networks, remote sensing images, object recognition, array processor, data-driven

CLC Number: