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Open AccessFeature PaperArticle

Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution

School of Computer Science and the Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an 710072, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1660;
Received: 2 April 2020 / Revised: 6 May 2020 / Accepted: 20 May 2020 / Published: 21 May 2020
(This article belongs to the Section Remote Sensing Image Processing)
Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.
Keywords: hyperspectral image; super-resolution (SR); convolutional neural networks (CNNs); mixed convolution; local feature fusion hyperspectral image; super-resolution (SR); convolutional neural networks (CNNs); mixed convolution; local feature fusion
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MDPI and ACS Style

Li, Q.; Wang, Q.; Li, X. Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution. Remote Sens. 2020, 12, 1660.

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