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

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

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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.

convolutional neural networksremote sensing imagesobject recognitionarray processordata-driven

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

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School of Electronic Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China

Laboratory of Integrated Circuit Design,Xi'an University of Science and Technology,Xi'an 710054,China

School of Computer Science and Technology,Xi'an University of Posts and Telecommunication,Xi'an 710121,China

This work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of ChinaShaanxi Provincial Co-ordination Innovation Project of Science and TechnologyShaanxi Provincial Key Research and Development Plan

61802304618340056177241761634004616023772016KTZDGY02-04-022017GY-060

2020

10.19682/j.cnki.1005-8885.2020.0048

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(6)
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