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Deep capsule network combined with spatial-spectral information for hyperspectral image classification

  • role： First author第一作者
  • Affiliation：

    Strategic Support Force Information Engineering University, Zhengzhou 450001, China

  • Email：gokling1219@163.com
  • Introduction：高奎亮，1996年生，男，硕士研究生， 研究方向为高光谱影像处理与分析。E-mail： gokling1219@163.com
GAO Kuiliang1,  
  • Affiliation：

    Strategic Support Force Information Engineering University, Zhengzhou 450001, China

YU Xuchu1,  
  • Affiliation：

    Institute of Kunming Physics, Kunming 650223, China

SONG Zhihang2,  
  • Affiliation：

    Institute of Kunming Physics, Kunming 650223, China

ZHANG Jin2,  
  • Affiliation：

    Strategic Support Force Information Engineering University, Zhengzhou 450001, China

LIU Bing1,  
  • Affiliation：

    Strategic Support Force Information Engineering University, Zhengzhou 450001, China

SUN Yifan1

Resümee

Owing to vectorized capsule neurons and dynamic routing algorithm, capsule network possesses stronger feature representation capability than traditional convolutional neural networks. In the field of remote sensing, hyperspectral image (HSI) classification methods based on capsule network have obtained better classification results than traditional deep learning models. Aiming at the problems in the capsule classification models, such as shallow network layers and insufficient utilization of spatial–spectral information, this paper constructs a new deep capsule network for HSI classification. The designed network utilizes convolutional capsule layer, residual connection, and three dimensional convolutional capsule layer to further improve classification accuracy.The proposed method includes a convolutional layer, four capsule residual blocks, a class capsule layer, and a reconstruction network. First, the proposed method takes HSI data cubes as the input directly to retain the spatial–spectral details in the HSI. Then, the deep features in the input data are extracted layer by layer with the capsule residual blocks. Finally, the three dimensional convolutional capsule layer is introduced to make full use of the spatial–spectral information, so as to improve the classification accuracy.Three public HSI data sets including University of Pavia, Indian Pines and Salinas, as well as a large-scale hyperspectral data set Chikusei, are selected for the experiments. The results demonstrate that the proposed method outperforms the existing deep learning classification methods. Compared with the shallow capsule network model and the deep three dimensional convolutional network with residual structure, the proposed method improves the overall classification accuracy by 1%—3%, 2%—5%, 1%—2%, and 2%—4% on four different data sets. In addition, hyperparameters such as learning rate, capsule neurons dimension, network structure, and spatial neighborhood have been analyzed in detail. The effectiveness of three dimensional convolutional capsule layer has also been proven by conducting ablation studies.Compared with the existing HSI classification model based on capsule network, the proposed method has the following advantages. (1) The high-dimensional cubes are used for input data without any dimension reduction process, thus enabling the model to make full use of spatial–spectral details. (2) A deep network is constructed utilizing convolutional capsule layers and residual connection, thus allowing the model to extract more robust and abstract deep features. (3) The introduction of three dimensional convolutional capsule layer can make full use of spatial–spectral information in HSI to further improve the classification accuracy.

Schlüsselwort

hyperspectral image classification;deep capsule network;three dimensional convolutional capsule;three dimensional convolutional routing;deep learning

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