Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (12): 93-99.doi: 10.11896/jsjkx.200700109

Special Issue: Software Engineering & Requirements Engineering for Complex Systems

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Study on DCGAN Model Improvement and SAR Images Generation

XU Yong-shi, BEN Ke-rong, WANG Tian-yu, LIU Si-jie   

  1. College of Electronic Engineering Navy University of Engineering Wuhan 430033,China
  • Received:2020-07-17 Revised:2020-09-10 Online:2020-12-15 Published:2020-12-17
  • About author:XU Yong-shi ,born in 1989 postgra-duate engineer.His main research in-terests include software quality assur-ance and software testing.
    BEN Ke-rong ,born in 1963 Ph.D pro-fessorPh.D supervisor is an outstan-ding member of China Computer Feder-ation.His main research interests in-clude software quality assurance and ar-tificial intelligence.
  • Supported by:
    13th Five-Year National Defense Pre-research Project of China(30201).

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Abstract: This paper proposes a method of generating SAR images based on the improved DCGAN.This method improves DCGANadopts the model structure of multi-generator versus single discriminatorand uses the algorithm to control the average image quality generated by each generator.In order to test and verify multiple similar image recognition software and select the best onetesters need to design the images that are different from those used in training to test the testing software.This method can provide a fair set of benchmarks for selective testing.Respectively in the experimentsbased on the original DCGAN model and the improved DCGAN modeltarget images and the images are generatedand the public discriminator is used to verify the quality of the new images generated by the two models.The experimental results show that the improved DCGAN model generates better images than the original DCGAN modeland the new SAR images have the same quality and better diversity as the original SAR imagesand they can meet the needs of software selective testing.

Key words: Automatic image generation, Generative adversarial network, Image quality detection, Image recognition, Software optimization

CLC Number: 

  • TP311.5
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