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Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification.

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    This study introduces a semi-supervised deep learning method for hyperspectral image classification, effectively using limited labeled data and abundant unlabeled data to train deep neural networks for improved accuracy.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Deep learning excels in computer vision but requires extensive labeled data for hyperspectral image classification.
    • Remote sensing applications often face a scarcity of labeled hyperspectral data due to high collection costs.

    Purpose of the Study:

    • To develop a semi-supervised deep learning approach for hyperspectral image classification using limited labeled and abundant unlabeled data.
    • To enhance the accuracy of hyperspectral image classification by leveraging both spectral and spatial information.

    Main Methods:

    • Utilized deep convolutional recurrent neural networks (CRNN) by treating hyperspectral pixels as spectral sequences.
    • Employed a semi-supervised framework incorporating pseudo-labels derived from unlabeled data for pre-training.
    • Introduced a constrained Dirichlet process mixture model (C-DPMM) for semi-supervised clustering to generate high-quality pseudo-labels, incorporating spatial constraints.

    Main Results:

    • The proposed semi-supervised method significantly improved hyperspectral image classification performance.
    • Pre-training with pseudo-labels and fine-tuning with labeled data led to superior results.
    • The C-DPMM effectively utilized spatial information, enhancing pseudo-label quality and network initialization.

    Conclusions:

    • Semi-supervised deep learning is a viable and effective strategy for hyperspectral image classification with limited labeled data.
    • The integration of spectral sequence modeling with spatial information via C-DPMM offers a powerful approach for improving classification accuracy.
    • The proposed method outperforms existing supervised and semi-supervised techniques on real hyperspectral datasets.