Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability of labeled data is limited due to the significant human resources and time costs of labeling hyperspectral data. Unsupervised learning for hyperspectral image classification has thus received increasing attention. In this paper, we propose a novel unsupervised framework based on a contrastive learning method and a transformer model for hyperspectral image classification. The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.
A color-modulating optical coating display based on phase change materials (PCM) and indium tin oxide (ITO) is fabricated and analyzed. We demonstrate that altering the thickness of top-ITO in this PCM-based display device can effectively change color. The significant role of the top-ITO layer in the thin-film interference in this multilayer system is confirmed by experiment as well as simulation. The ternary-color modulation of devices with only 5 nano thin layer of phase change material is achieved. Furthermore, simulation work demonstrates that a stirringly broader color gamut can be obtained by introducing the control of the top-ITO thickness.
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