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A hyperspectral image projector for hyperspectral imagers
SPIE Proceedings, 2007We have developed and demonstrated a Hyperspectral Image Projector (HIP) intended for system-level validation testing of hyperspectral imagers, including the instrument and any associated spectral unmixing algorithms. HIP, based on the same digital micromirror arrays used in commercial digital light processing (DLP*) displays, is capable of projecting
Joseph P. Rice +3 more
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SPIE Proceedings, 2010
VTT Technical Research Centre of Finland has developed a new low cost hand-held staring hyperspectral imager for applications previously blocked by high cost of the instrumentation. The system is compatible with standard video and microscope lenses. The instrument can record 2D spatial images at several wavelength bands simultaneously.
Aallos, Ville-Veikko +5 more
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VTT Technical Research Centre of Finland has developed a new low cost hand-held staring hyperspectral imager for applications previously blocked by high cost of the instrumentation. The system is compatible with standard video and microscope lenses. The instrument can record 2D spatial images at several wavelength bands simultaneously.
Aallos, Ville-Veikko +5 more
openaire +3 more sources
Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral–spatial kernel features poses a challenge ...
S. K. Roy +3 more
semanticscholar +1 more source
Hyperspectral Anomaly Detection With Robust Graph Autoencoders
IEEE Transactions on Geoscience and Remote Sensing, 2022Anomaly detection of hyperspectral data has been gaining particular attention for its ability in detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants can not only extract intrinsic features automatically but also ...
Ganghui Fan +5 more
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Deep Hierarchical Vision Transformer for Hyperspectral and LiDAR Data Classification
IEEE Transactions on Image Processing, 2022In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture for hyperspectral and light detection and ranging (LiDAR) data joint classification.
Zhixiang Xue +5 more
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Enhanced Deep Image Prior for Unsupervised Hyperspectral Image Super-Resolution
IEEE Transactions on Geoscience and Remote SensingDepending on a large-scale paired dataset of low-resolution hyperspectral image (LrHSI), high-resolution multispectral image (HrMSI), and corresponding high-resolution hyperspectral image (HrHSI), the supervised paradigm has achieved impressive ...
Jiaxin Li +5 more
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Hyperspectral Image Super-Resolution Meets Deep Learning: A Survey and Perspective
IEEE/CAA Journal of Automatica Sinica, 2023Hyperspectral image super-resolution, which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation, aims to improve the spatial resolution of the hyperspectral image, which is beneficial for subsequent ...
Xinya Wang +3 more
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IEEE Transactions on Geoscience and Remote Sensing
Recently, vision transformer (ViT)-based deep learning (DL) models have achieved remarkable performance gains in hyperspectral image classification (HSIC) due to their abilities to model long-range dependencies and extract global spatial features ...
Zhuoyi Zhao +3 more
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Recently, vision transformer (ViT)-based deep learning (DL) models have achieved remarkable performance gains in hyperspectral image classification (HSIC) due to their abilities to model long-range dependencies and extract global spatial features ...
Zhuoyi Zhao +3 more
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IEEE Transactions on Geoscience and Remote Sensing, 2022
Currently, the different deep neural network (DNN) learning approaches have done much for the classification of hyperspectral images (HSIs), especially most of them use the convolutional neural network (CNN).
U. Bhatti +9 more
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Currently, the different deep neural network (DNN) learning approaches have done much for the classification of hyperspectral images (HSIs), especially most of them use the convolutional neural network (CNN).
U. Bhatti +9 more
semanticscholar +1 more source
Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion
IEEE Geoscience and Remote Sensing Letters, 2022Hyperspectral images (HSIs) usually have finer spectral resolution but coarser spatial resolution than multispectral images (MSIs). To obtain a desired HSI with higher spatial resolution, great research attention has been paid to achieving hyperspectral ...
Jiaxin Li +4 more
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