سابِق|تال

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

ملخص

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.

مفهوم

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

References

  1. 1.
    Aydav P S S and Minz S. 2018. Classification of hyperspectral images using self-training and a pseudo validation set. Remote Sensing Letters, 9(11): 1109-1117
  2. 2.
    Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N and Chanussot J. 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6-36
  3. 3.
    Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16
  4. 4.
    Bruzzone L, Chi M and Marconcini M. 2006. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3363-3373
  5. 5.
    Camps-Valls G, Bandos Marsheva T V and Zhou D Y. 2007. Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45(10): 3044-3054
  6. 6.
    Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Vila-Frances J and Calpe-Maravilla J. 2006. Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 3(1): 93-97
  7. 7.
    Chen P H, Jiao L C, Liu F, Zhao J Q, Zhao Z Q and Liu S. 2017. Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction. Pattern Recognition, 61: 361-378
  8. 8.
    Cheung E and Li Y Y. 2017. Self-training with adaptive regularization for S3VM//2017 International Joint Conference on Neural Networks (IJCNN). Anchorage: IEEE: 3633-3640
  9. 9.
    Dong Y N, Jin Y and Cheng S B. 2022. Clustered multiple manifold metric learning for hyperspectral image dimensionality reduction and classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5516813
  10. 10.
    Gan H T, Li Z H, Wu W, Luo Z Z and Huang R. 2018. Safety-aware graph-based semi-supervised learning. Expert Systems with Applications, 107: 243-254
  11. 11.
    Ge H M, Pan H Z, Wang L G, Li C, Liu Y Z, Zhu W L and Teng Y P. 2021. A semi-supervised learning method for hyperspectral imagery based on self-training and local-based affinity propagation. International Journal of Remote Sensing, 42(17): 6391-6416
  12. 12.
    Ghamisi P, Plaza J, Chen Y S, Li J and Plaza A J. 2017. Advanced spectral classifiers for hyperspectral images: a review. IEEE Geoscience and Remote Sensing Magazine, 5(1): 8-32
  13. 13.
    Gu S K and Jin Y C. 2017. Multi-train: a semi-supervised heterogeneous ensemble classifier. Neurocomputing, 249: 202-211
  14. 14.
    Gu X W. 2020. A self-training hierarchical prototype-based approach for semi-supervised classification. Information Sciences, 535: 204-224
  15. 15.
    Hong D F, Yokoya N, Ge N, Chanussot J and Zhu X X. 2019. Learnable manifold alignment (LeMA): a semi-supervised cross-modality learning framework for land cover and land use classification. ISPRS Journal of Photogrammetry and Remote Sensing, 147: 193-205
  16. 16.
    Jin Y, Dong Y N, Zhang Y X and Hu X Y. 2022. SSMD: dimensionality reduction and classification of hyperspectral images based on spatial-spectral manifold distance metric learning. IEEE Transactions on Geoscience and Remote Sensing, 60: 5538916
  17. 17.
    Li F, Clausi D A, Xu L L and Wong A. 2018. ST-IRGS: a region-based self-training algorithm applied to hyperspectral image classification and segmentation. IEEE Transactions on Geoscience and Remote Sensing, 56(1): 3-16
  18. 18.
    Li Z Y, Togo R, Ogawa T and Haseyama M. 2020. Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. Medical and Biological Engineering and Computing, 58(6): 1239-1250
  19. 19.
    Lu X C, Zhang J P, Li T and Zhang Y. 2016. A novel synergetic classification approach for hyperspectral and panchromatic images based on self-learning. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4917-4928
  20. 20.
    Plaza A, Benediktsson J A, Boardman J W, Brazile J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P, Gualtieri A, Marconcini M, Tilton J C and Trianni G. 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113: S110-S122
  21. 21.
    Tan K, Zhu J S, Du Q, Wu L X and Du P J. 2016. A novel tri-training technique for semi-supervised classification of hyperspectral images based on diversity measurement. Remote Sensing, 8(9): 749
  22. 22.
    van Engelen J E and Hoos H H. 2020. A survey on semi-supervised learning. Machine Learning, 109(2): 373-440
  23. 23.
    Wang F and Zhang C S. 2008. Label propagation through linear Neighborhoods. IEEE Transactions on Knowledge and Data Engineering, 20(1): 55-67
  24. 24.
    Wang L G, Hao S Y, Wang Q M and Wang Y. 2014. Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation. ISPRS Journal of Photogrammetry and Remote Sensing, 97: 123-137
  25. 25.
    Wu D, Shang M S, Luo X, Xu J, Yan H Y, Deng W H and Wang G Y. 2018. Self-training semi-supervised classification based on density peaks of data. Neurocomputing, 275: 180-191
  26. 26.
    Zerguine A, Shafi A and Bettayeb M. 2001. Multilayer perceptron-based DFE with lattice structure. IEEE Transactions on Neural Networks, 12(3): 532-545
  27. 27.
    Zhang L, Zhou W D and Jiao L C. 2004. Wavelet support vector machine. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1): 34-39
  28. 28.
    Zhou Z H and Li M. 2005. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11): 1529-1541
  29. 29.
    Zoidi O, Tefas A, Nikolaidis N and Pitas I. 2018. Positive and negative label propagations. IEEE Transactions on Circuits and Systems for Video Technology, 28(2): 342-355

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