Results 11 to 20 of about 1,077,071 (292)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral compressive imaging has taken advantage of compressive sensing theory to capture spectral information of the dynamic world in recent decades of years, where an optical encoder is employed to compress high dimensional signals into a single 2-
Hao Xiang +6 more
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Remote Sensing Imagery Object Detection Model Compression via Tucker Decomposition
Mathematics, 2023 Although convolutional neural networks (CNNs) have made significant progress, their deployment onboard is still challenging because of their complexity and high processing cost.
Lang Huyan +10 more
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Hyperspectral Image Denoising via
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThis article studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the $L_{1}$-norm-based regularizers to remove sparse noise and ...
Xin Tian, Kun Xie, Hanling Zhang
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Applied SciencesAccurate temperature measurement in coal-fired power plants is crucial for optimizing combustion and achieving deep load regulation. While acoustic temperature measurement is an efficient and stable method, its practical application is limited to two ...
Jidong Yan +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Change detection from multitemporal hyperspectral images has attracted great attention. Most traditional methods using spectral information for change detection treat a hyperspectral image as a two-dimensional matrix and do not take into account ...
Zengfu Hou, Wei Li, Ran Tao, Qian Du
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Incremental Hierarchical Tucker Decomposition
arXiv.orgWe present two new algorithms for approximating and updating the hierarchical Tucker decomposition of tensor streams. The first algorithm, Batch Hierarchical Tucker - leaf to root (BHT-l2r), proposes an alternative and more efficient way of approximating
Doruk Aksoy, A. Gorodetsky
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Efficient enhancement of low-rank tensor completion via thin QR decomposition [PDF]
Frontiers in Big DataLow-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues.
Yan Wu, Yunzhi Jin
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EEG multi-domain feature transfer based on sparse regularized Tucker decomposition. [PDF]
Cogn Neurodyn, 2023 Gao Y, Zhang C, Huang J, Meng M.
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Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
Applied SciencesThe human functional connectome (FC) is a representation of the functional couplings between brain regions derived from blood oxygen level-dependent (BOLD) signals.
Vitor Carvalho +4 more
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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