高光谱成像
人工智能
增采样
图像分辨率
计算机科学
卷积神经网络
模式识别(心理学)
RGB颜色模型
计算机视觉
图像融合
全光谱成像
水准点(测量)
图像(数学)
地理
大地测量学
作者
Xian‐Hua Han,Boxin Shi,Yinqiang Zheng
标识
DOI:10.1109/icip.2018.8451142
摘要
Fusing a low-resolution hyperspectral image with the corresponding high-resolution RGB image to obtain a high-resolution hyperspectral image is usually solved as an optimization problem with prior-knowledge such as sparsity representation and spectral physical properties as constraints, which have limited applicability. Deep convolutional neural network extracts more comprehensive features and is proved to be effective in upsampling RGB images. However, directly applying CNNs to upsample either the spatial or spectral dimension alone may not produce pleasing results due to the neglect of complementary information from both low resolution hyper spectral and high resolution RGB images. This paper proposes two types of novel CNN architectures to take advantages of spatial and spectral fusion for hyperspectral image superresolution. Experiment results on benchmark datasets validate that the proposed spatial and spectral fusion CNNs outperforms the state-of-the-art methods and baseline CNN architectures in both quantitative values and visual qualities.
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