HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on.
D. Li, L. Xu, J. Peng, J. Ma
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Intraoperative hyperspectral imaging (HSI) as a new diagnostic tool for the detection of cartilage degeneration [PDF]
Scientific Reports, 2022 AbstractTo investigate, whether hyperspectral imaging (HSI) is able to reliably differentiate between healthy and damaged cartilage tissue. A prospective diagnostic study was performed including 21 patients undergoing open knee surgery. HSI data were acquired during surgery, and the joint surface’s cartilage was assessed according to the ICRS cartilage
Max Kistler +6 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications ...
Zhen Zhang +3 more
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The role of intraoperative hyperspectral imaging (HSI) in colon interposition after esophagectomy
BMC Surgery, 2023 Abstract Background Colon conduit is an alternative approach to reconstructing the alimentary tract after esophagectomy. Hyperspectral imaging (HSI) has been demonstrated to be effective for evaluating the perfusion of gastric conduits, but not colon conduits.
Anne Zimmermann +10 more
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Graph Convolutional Sparse Subspace Coclustering With Nonnegative Orthogonal Factorization for Large Hyperspectral Images [PDF]
, 2022 International audienceSparse subspace clustering (SSC) is a representative data clustering paradigm that has been broadly applied in the unsupervised classification of hyperspectral images (HSIs).
Xiao, Liang +3 more
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Bayesian Subpixel Mapping Neural Network for Hyperspectral images [PDF]
, 2023 Subpixel mapping (SPM) of a hyperspectral image (HSI) allocates land cover fractions or discrete abundances in original pixels, so that the resolution of the HSI label map becomes finer by dividing the mixed pixel to subpixels.
Wang, Yuxian +3 more
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Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction [PDF]
, 2021 Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs).
Maoguo Gong +5 more
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Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning
IEEE Access, 2019 Hyperspectral unmixing is an important and challenging task in the field of remote sensing which arises when the spatial resolution of sensors is insufficient for the separation of spectrally distinct materials.
Burkni Palsson +2 more
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Hyperspectral Image Restoration with Self-supervised Learning: A Two-stage Training Approach [PDF]
, 2021 Hyperspectral image (HSI) denoising is a crucial preprocessing task to improve the performance of the subsequent HSI interpretation and applications.
Chen, L, Zhou, J, Zhu, H, Qian, Y
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Sensors, 2023 Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs).
Zelin Li, Wenhong Wang
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