高光谱成像
人工智能
卷积神经网络
计算机科学
模式识别(心理学)
特征提取
卷积(计算机科学)
图像分辨率
特征(语言学)
冗余(工程)
直方图
计算机视觉
图像(数学)
人工神经网络
哲学
操作系统
语言学
作者
Sen Jia,Shuangzhao Zhu,Wenwen Wang,Meng Xu,Weixi Wang,Yujuan Guo
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-15
被引量:7
标识
DOI:10.1109/tgrs.2023.3250640
摘要
With the rapid development of deep convolutional neural networks, super-resolution (SR) in hyperspectral image (HSI) has achieved good results. Current methods generally use two dimensional (2D) convolution for feature extraction, but they cannot effectively extract spectral information. Although three dimensional (3D) convolution can better characterize feature structure of HSI, it will lead to parameter redundancy, model complexity and severe memory shortage. To address above problems, we propose a new hyperspectral image super-resolution method, named diffused convolutional neural network (DCNN). Specifically, spectral convolutions have been added into the enhanced convolutional neural (ECN) block, and a series of spectral convolutions is introduced in the residual network to learn features in the channel direction of different depths. Further, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to retain the shape and texture information of the image respectively, which can well represent the spatial structure of the object. In order to effectively make use of the extracted shallow and deep features, a feature fusion strategy is employed to reinforce the reconstruction efficiency. Besides, an image enhancement module has been developed to diffuse the super-resolution image into the image space. Extensive evaluations and comparisons show that our DCNN approach can not only recover the HSI data with richer details, but also achieve superiority over several state-of-the-art methods.
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