Augmentation Technology of Remote Sensing Dataset Based on Improved DCGAN Algorithm
Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 80-84.doi: 10.11896/jsjkx.200700185

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Augmentation Technology of Remote Sensing Dataset Based on Improved DCGAN Algorithm

ZHANG Man, LI Jie, ZHU Xin-zhong, SHEN Ji, CHENG Hao-tian   

  1. Shanghai Aerospace Electronics Technology Research Institute,Shanghai 201109,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZHANG Man,born in 1993,master's.Her main research interests include image processing and remote sensing application.

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Abstract: The scale of the remote sensing dataset is a crucial factor to object detection algorithm's performance based on deep learning technology.How to generate large number of labeled images by using a small amount of data has become a hot research topic.To solve the problem,we propose an augmenting method of remote sensing dataset based on improved DCGAN algorithm using the secondary mask technology.In addition,the algorithmproposed in this paper can realize the amplification of images and labels by determining the number and location of targets to be generated,to solve the problem of no corresponding label generation in GAN-based image augmentation algorithm.Moreover,a multi-scale feature fusion technique for optimizing DCGAN algorithm is proposed to solve the problem of poor image quality generated by DCGAN algorithm.Experiments show that the improved DCGAN algorithm is superior to the DCGAN algorithm on both MNIST and PlANE datasets in terms of image quality and image diversity.The AP value of the dataset expanded based on the method we arranged is up to 84.45% in the design experiment using the Tiny-YoloV2 algorithm.Compared with the unaugmented algorithm and the traditional augmented method,the AP value of the method adopted in this paper is increased by 16.05% and 2.88% respectively,which fully verifies the effectiveness of the the technical scheme designed in this paper.

Key words: Augmentation of dataset, DCGAN, Feature fusion, Multi-feature map, Object detection, Remote sensing images

CLC Number: 

  • TP751
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