Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 224-233.DOI: 10.3778/j.issn.1002-8331.2007-0080

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Transfer Learning for Hyperspectral Image Classification Using Homogeneous Area Characteristics

ZHOU Shaoguang, WU Hao, ZHAO Chanjuan, CHEN Renxi   

  1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
  • Online:2021-11-01 Published:2021-11-04



  1. 河海大学 地球科学与工程学院,南京 211100


Deep convolutional neural networks can fully utilize the inner connections between features which improve the separability of the hyperspectral images and have been widely studied in recent years. However, the need for an abundant labeled sample for training deep network models limits the application of such methods. To cut the need for the number of labeled samples, the technology of transfer learning is adopted for remote sensing classification in this paper. The one-shot problem is concerned in which only a single sample of each class is available. Data augmentation is carried out, based on homogenous regions obtained by image segmentation. Using the deep Siamese CNN trained by augmentation samples, this paper can cut the divergence between the distributions of the source domain and target domain, and classify the target image. Experiment results based on cross-region hyperspectral image datasets show the effect of the combination of deep Siamese CNN and homogenous areas on semi-supervised transfer learning.

Key words: hyperspectral image classification, siamese neural network, image segmentation, homogeneous area, one-shot learning, semi-supervised transfer learning



关键词: 高光谱图像分类, 孪生神经网络, 图像分割, 同质区, 单样本学习, 半监督迁移学习