Xiaochen Lu
Donghua University
5 Papers
9 Citations
Xiaochen Lu is an academic researcher from Donghua University. The author has contributed to research in topics: Hyperspectral imaging & Convolutional neural network. The author has an hindex of 1, co-authored 5 publications.
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Papers
Cascaded Convolutional Neural Network-Based Hyperspectral Image Resolution Enhancement via an Auxiliary Panchromatic Image
TL;DR: Wang et al. as mentioned in this paper proposed a two-stage cascaded CNN to reconstruct the anticipated high-resolution hyperspectral (HS) image, which can improve the spatial resolution and spectral fidelity of HS image, and achieve better performance than some state-of-theart HS pan-sharpening algorithms.
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Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network
TL;DR: In this article, a spatial correlation regularized unmixing convolutional neural network (CNN) was proposed to explore the collaborative spatial and spectral information of an hyperspectral image and infer the high-resolution abundance maps, thereby reconstructing the anticipated highresolution HS image via the linear spectral mixture model, and a dual-branch architecture network and spatial spread transform function were employed to characterize the spatial correlation between the high and low-resolution HS images, aiming at promoting the fidelity of the super-resolved image.
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Hyperspectral Image Classification Based on Multilevel Joint Feature Extraction Network
TL;DR: Wang et al. as discussed by the authors proposed a multilevel joint feature extraction network, which makes full use of the information on each channel of HSI and transforms it into valid channel-wised spatial features through a designed convolution process.
Coupled Convolutional Neural Network-Based Detail Injection Method for Hyperspectral and Multispectral Image Fusion
TL;DR: Compared with some state-of-the-art HS and MS image fusion methods, the proposed method achieves better fusion results, provides excellent spectrum preservation ability, and is easy to implement.
8
A Novel Unmixing-Based Hypersharpening Method via Convolutional Neural Network
TL;DR: A novel unmixing-based HS and MS image fusion method, via a convolutional neural network (CNN), is proposed to promote spectral fidelity and is beneficial to the spectral fidelity of the fused images compared with some state-of-the-art algorithms.