Hyperspectral Image Classification Using Semi-supervised Deep Learning Strategies

Recent development in deep learning (DL) methodologies has shown promising results on various computer vision tasks including the classification of hyperspectral data. However, these methodologies are expected to suffer in the presence of lack of training data, due to complex network architecture and a large number of parameters. In this paper, various K-means-based clustering techniques are explored to generate pseudo-labels to facilitate the training of deep networks. To tackle the curse of dimensionality, an auto-encoder (AE)-based dimensionality reduction method is employed. Finally, the classification is done using convolutional long short-term memory cells (ConvLSTM) which outperforms the rest of the deep neural networks used. In addition, an analysis of the effect of the proposed dimensionality reduction method on classification accuracy is presented. The efficacy of the proposed approach is demonstrated on two real-world hyperspectral image datasets namely the “University of Pavia” (UP) and “Salinas”.