Journal of Xidian University ›› 2021, Vol. 48 ›› Issue (5): 128-138.doi: 10.19665/j.issn1001-2400.2021.05.016

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Application of the deep fusion mechanism in object detection of remote sensing images

DONG Ruchan1,2(),JIAO Licheng3(),ZHAO Jin4(),SHEN Weiyan1()   

  1. 1. School of Software Engineering,Jinling Institute of Technology,Nanjing 211169,China
    2. Software Testing Engineering Laboratory of Jiangsu Province,Nanjing 211169,China
    3. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China,Xidian University,Xi’an 710071,China
    4. School of Mathematics and Statistics,Xi’an JiaoTong University,Xi’an 710049,China
  • Received:2021-07-15 Online:2021-10-20 Published:2021-11-09

Abstract:

A new target detection technology for remote sensing images based on the deep fusion mechanism is proposed,which combines the multi-scale,attention mechanism and broad learning system based on the deep convolutional neural network.This technology focuses effectively on the high-level semantic information of remote sensing images and the characteristics of small targets.Because of the problem of manual adjustment of hyperparameters in the broad learning system,the author proposes a broad learning system based on the Bayesian network search,which can learn intelligently.A set of parameter values applicable to different remote sensing images can efficiently identify targets.Compared with other state-of-the-art methods,experimental results show that this technology can effectively solve the problems of a slow detection speed,a low recognition accuracy,and small targets in remote sensing image target detection tasks.

Key words: deep convolution neural network, remote sensing image, attention mechanism, broad learning system, object detection

CLC Number: 

  • TP391.41