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Fast self-training based on spatial-spectral information for hyperspectral image classification

  • role： First author第一作者
  • Affiliation：

    Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

    Hubei Luojia Laboratory, Wuhan 430079, China

    State Key Caboratory of Information Engineering in Surveying , Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Email：yao.jin@whu.edu.cn
  • Introduction：金垚，研究方向为高光谱遥感影像降维与分类。E-mail： yao.jin@whu.edu.cn
JIN Yao123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

    Hubei Luojia Laboratory, Wuhan 430079, China

    School of Resource and Environmental Science, Wuhan University, Wuhan 430079,China

  • Email：dongyanni@cug.edu.cn
  • Introduction：董燕妮，研究方向为遥感图像智能解译。E-mail： dongyanni@cug.edu.cn
DONG Yanni124*,  
  • Affiliation：

    School of Computer Science, Wuhan University, Wuhan 430072, China

DU Bo5

résumé

Hyperspectral image classification has been a popular issue in the field of hyperspectral image interpretation. The prominent problem in hyperspectral image classification currently involves the considerably time-consuming and expensive manual acquisition of labeled samples for hyperspectral images in practical applications. This problem leads to a sparse number of training samples and increases the difficulty of obtaining good classification results. Self-training methods are widely used in hyperspectral image classification to solve the difficulty in labeled sample acquisition in hyperspectral image classification. Traditional self-training methods mostly use spectral information to classify unlabeled samples and then utilize the expanded labeled data set to iteratively train the classifier to complete the classification task. In this model, the spatial information provided by the hyperspectral images is ignored, resulting in poor classification accuracy. Simultaneously, the classification of unlabeled data must be completed once during each iteration, resulting in a significant time cost. Therefore, a fast self-training method based on spatial–spectral information is proposed in this paper for hyperspectral image classification to address the above problems.FST-SS (Fast Self-training based on Spatial-Spectral information) supplements the spatial information in hyperspectral images by exploiting the consistency of the spatial distribution of features in the hyperspectral images. Instead of using the classifier to classify unlabeled samples, this approach uses the spatial–spectral information to filter unlabeled data and extend the labeled samples during the iterative process. The spatial nearest neighbors are first selected using a spatial domain patch for the initial labeled sample. The spatial nearest neighbors are then filtered using an adaptive threshold to obtain the spatial–spectral nearest neighbors to be labeled. Finally, the classifier is trained to complete the classification task based on the expanded labeled samples.This paper compares FST-SS with the supervised classification algorithm 1NN, the semi-supervised classification algorithm Star-SVM, Tri-Training, ST-DP, and LeMA on two real hyperspectral datasets to demonstrate the effectiveness of FST-SS. Experimental results show that the overall classification accuracy reaches 93.17% and 95.43% when 2 and 10 training samples, respectively, are selected for each class in the Washington DC Mall subimage dataset. The overall classification accuracy in Indian Pines dataset reaches 59.75% and 86.13%, which is a significant improvement compared with the comparison algorithm.The FST-SS algorithm uses the spatial–spectral information provided by hyperspectral images to label unlabeled samples by combining the ideas of self-training methods. Compared with the conventional self-training methods, instead of using a classifier for classification, FST-SS uses the spatial–spectral information to filter the unlabeled samples directly, which markedly improves the computational efficiency of the algorithm.

mots-clés

hyperspectral image classification;semi-supervised classification;spatial-spectral information;self-training

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