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CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification

  • role： First author第一作者
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhangkang1128@126.com
  • Introduction：张康(1992—　)，女，硕士研究生，研究方向为遥感图像处理、机器学习等。E-mail：zhangkang1128@126.com
Kang ZHANG123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

  • Email：heibq@csu.ac.cn
  • Introduction：黑保琴(1975—　)，女，高级工程师，研究方向为信号与信息处理。E-mail：heibq@csu.ac.cn
Baoqin HEI12*,  
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

Zhuang ZHOU12,  
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

Shengyang LI12

résumé

Hyperspectral remote Sensing Images (HSIs), which contain rich spectral and spatial information, are important in the precise classification of earth objects. However, HSIs are usually highly dimensional and non-linear, and they contain large amounts of data. These characteristics increase the difficulty of data processing and bring about the Hughes phenomenon. Conventional methods such as Neural Networks (NN) and support vector machine have solved these problems by reducing the dimensions of HSIs with PCA, ICA, or MNF. Although these methods are effective in the classification of HSIs, they may cause information loss in the original data. Therefore, improving the accuracy of HSI classification with inadequate data is difficult. Recently, deep learning method, especially Convolutional Neural Network (CNN), has achieved remarkable performance in many fields. Therefore, the application of CNNs to HSI classification shows immense potential. To avoid the Hughes phenomenon and improve the accuracy of his classification, this study proposes a Coefficient of Variation–Convolution Neural Network (CV-CNN) method for the classification of HSIs. After the calculation of the Coefficient of Variation of the IntrA-class (CVIA) and the Coefficient of Variation of the IntEr-class (CVIE) of each band, the bands with low (CVIE)2/CVIA values are excluded. Then, a target pixel with the spectral information of its eight neighbors is organized as multi-layer spectral–spatial information. The spectral–spatial information of the target pixel should then be converted into matrix form. The two-dimensional image suitable for the input of CNN was subsequently obtained. Furthermore, a seven-layer CNN model was constructed with two convolution layers, two max-pooling layers, two full-connection layers, and one softmax layer. Using the seven-layer CNN can effectively improve the accuracy of HSI classification. Experiments were conducted on the Indian Pines dataset and Pavia University dataset to evaluate the performance of the presented CV-CNN method. The results are as follows: (1) Compared with the method that only considers spectral information for HSI classification, the use of spectral–spatial information can actually improve the accuracy of HSI classification. (2) Compared with other CNN methods, the seven-layer CNN model with no band removed can increase the overall accuracy by 2.61% for the Indian Pines dataset and 0.04% for the Pavia University dataset. (3) Based on the same seven-layer model, the experiments show that the classification of HSI with poor bands excluded shows increased accuracy from 97.9% to 98.69% for the Indian Pines dataset and from 99.6% to 99.66% for the Pavia University dataset. This outcome verifies the validity of excluding poor bands on the basis of the CV method. (4) Based on the same seven-layer model, the experiments show that the CV method, compared with the PCA and MNF methods, can increase the overall accuracy by 1.38% and 0.44%, respectively, for the Indian Pines dataset and by 3.26% and 1.83%, respectively, for the Pavia University dataset. The CNN model developed in this study and the method of removing poor bands with the CV technique can improve the accuracy of HSI classification.

mots-clés

Convolutional Neural Networks(CNN);deep learning;coefficient of variation;hyperspectral remote sensing images;classification

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