高光谱成像
计算机科学
人工智能
卷积神经网络
图像(数学)
模式识别(心理学)
深度学习
图像分辨率
卷积(计算机科学)
任务(项目管理)
人工神经网络
计算机视觉
作者
Zhaori Gong,Nannan Wang,De Cheng,Xinrui Jiang,Jingwei Xin,Xi Yang,Xinbo Gao
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-14
标识
DOI:10.1109/tgrs.2022.3185647
摘要
Hyperspectral image super-resolution (HSISR) task has been widely studied, and significant progress has been made by leveraging the deep convolution neural network (CNN) techniques. Nevertheless, the scarcity of training images hinders the research progress of HSISR task. Moreover, the differences in imaging conditions and the number of spectral bands among different datasets, make it very difficult to construct a unified deep neural network. In this paper, we first present a non-training based HSISR method based on deep prior knowledge, which captures the image prior to restore the high resolution image by using the intrinsic characteristics of CNN. Then, we append a special network input processing module onto the HSI super-resolution network to automatically adjust the structure of the input so that the choice of network structure is no longer limited, while the network design focuses on exploiting the spatial information of hyperspectral images and the correlation between spectral bands, making the method more suitable for HSISR tasks and greatly extending its applications. Extensive experiment results on the hyperspectral image datasets illustrate the effectiveness of the proposed method, and we have got comparable results with the state-of-the-art methods while requiring no training samples.
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