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Space plant image segmentation based on generative adversarial network
Author(s): Xianfeng Wang; Ye Li; Zhen Yan; Lili Guo; Shan Jin
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Paper Abstract

The study of plants in space plays an important role in serving astronauts. Automatic segmentation of space plant image (SPI) provides an effective method for studying plants, and many plant segmentation methods have been proposed. However, segmentation of SPI is still challenging. Because the number of SPI is small, which greatly increases the difficulty in model training (especially deep-network-based model). For dealing with this problem, we propose a plant segmentation method based on a generative adversarial network. Our method consists of a generative network (GN) and a discriminant network (DN). The GN firstly extracts features from an input image, and then generates a feature map by developing multiple convolution and deconvolution layers. The DN merges the feature map with an actual plant image, and then computes a segmentation result by a deep convolutional network. In DN, the addition of the feature map improves segmentation accuracy of DN, and reduces the requirements of training images during training DN. Several experiments are made, and the experimental results show that our method performs well when a small number of training images is provided for model training.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692G (6 May 2019); doi: 10.1117/12.2524262
Show Author Affiliations
Xianfeng Wang, Technology and Engineering Ctr. for Space Utilization (China)
Univ. of Chinese Academy of Sciences (China)
Ye Li, Technology and Engineering Ctr. for Space Utilization (China)
Zhen Yan, Technology and Engineering Ctr. for Space Utilization (China)
Lili Guo, Technology and Engineering Ctr. for Space Utilization (China)
Tsinghua Univ. (China)
Shan Jin, Technology and Engineering Ctr. for Space Utilization (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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