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Technology of Graphic & Image
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3834-3840,3847

MaskMix:mask mixing augmentation method for change detection

Xing Yana
Wei Jiedab
Wang Ruofeib
Huang Ruib
a. School of Safety Science & Engineering, b. School of Computer Science & Technology, Civil Aviation University of China, Tianjin 300300, China

Abstract

Data augmentation is a key technique to improve the generalizability of change detection models. Although the existing data augmentation methods achieve promising performance in image classification and object detection, they ignore the differences among the time-series images and the diversities of the changed objects. In order to preserve the change region and increase the information of the complex background, this paper proposed a novel data augmentation method for change detection based on change region mask, called MaskMix. Firstly, the change regions of the current image pair were pasted on an image pair to generate a new change image pair having new background and new changes. Secondly, MaskMix further augmented the image pair by multi-path weighted fusion strategy. It selected a classical image processing method randomly from an image processing set for each path to conduct further augmentation. And then the processed image pairs from K paths were fused using a K-dimensional weight generated by Dirichlet distribution. Finally, the pre-processed image pair and the post-processed image pair were fused by the weight generated by the Beta distribution through the skip connection. Experiments conducted on two publicly available datasets, e. g., BCD(build change detection) and LEVIR-CD(LEVIR building change detection dataset), demonstrate that MaskMix significantly improves the generalizability of change detectors, e. g., ADCDNet, BIT, ChangeFormer, SNUNet, and DSAMNet. Moreover, compared with the existing image augmentation methods, such as MixUp, AugMix, MUM, and CropMix, MaskMix effectively increases the complexity and diversity of change images, enhancing the generalizability of existing change detection methods.

Foundation Support

中央高校基本科研业务费项目中国民航大学专项资助项目(3122022091)
中国民航大学科研启动项目(2017QD15X,2017QD17X)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0228
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Technology of Graphic & Image
Pages: 3834-3840,3847
Serial Number: 1001-3695(2023)12-050-3834-07

Publish History

[2023-08-14] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

邢艳, 魏接达, 汪若飞, 等. MaskMix:用于变化检测的掩码混合数据增强方法 [J]. 计算机应用研究, 2023, 40 (12): 3834-3840,3847. (Xing Yan, Wei Jieda, Wang Ruofei, et al. MaskMix:mask mixing augmentation method for change detection [J]. Application Research of Computers, 2023, 40 (12): 3834-3840,3847. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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