[HTML][HTML] Feature construction methods for processing and analysing spectral images and their applications in food quality inspection
Background Hyperspectral imaging (HSI) technology fusing spectroscopic technology and
imaging technology has been proposed to achieve rapid and non-destructive inspection of …
imaging technology has been proposed to achieve rapid and non-destructive inspection of …
Hyperspectral image super-resolution meets deep learning: A survey and perspective
Hyperspectral image super-resolution, which refers to reconstructing the high-resolution
hyperspectral image from the input low-resolution observation, aims to improve the spatial …
hyperspectral image from the input low-resolution observation, aims to improve the spatial …
Crop type classification by DESIS hyperspectral imagery and machine learning algorithms
N Farmonov, K Amankulova, J Szatmári… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Developments in space-based hyperspectral sensors, advanced remote sensing, and
machine learning can help crop yield measurement, modelling, prediction, and crop …
machine learning can help crop yield measurement, modelling, prediction, and crop …
ESSAformer: Efficient transformer for hyperspectral image super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
Exploring the relationship between 2D/3D convolution for hyperspectral image super-resolution
Hyperspectral image super-resolution (SR) methods based on deep learning have achieved
significant progress recently. However, previous methods lack the joint analysis between …
significant progress recently. However, previous methods lack the joint analysis between …
An efficient unfolding network with disentangled spatial-spectral representation for hyperspectral image super-resolution
Hyperspectral image super-resolution (HSI SR) is dramatically impacted by high spectral
dimensionality, insufficient spatial resolution, and limited availability of training samples …
dimensionality, insufficient spatial resolution, and limited availability of training samples …
[HTML][HTML] Mixed 2D/3D convolutional network for hyperspectral image super-resolution
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 …
great success recently. However, there are two main problems in the previous works. One is …
Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution
Hyperspectral imaging is unable to acquire images with high resolution in both spatial and
spectral dimensions yet, due to physical hardware limitations. It can only produce low spatial …
spectral dimensions yet, due to physical hardware limitations. It can only produce low spatial …
Hyperspectral image superresolution using spectrum and feature context
Deep learning-based hyperspectral image superresolution methods have achieved great
success recently. However, most methods utilize 2D or 3D convolution to explore features …
success recently. However, most methods utilize 2D or 3D convolution to explore features …
Variational regularization network with attentive deep prior for hyperspectral–multispectral image fusion
Hyperspectral–multispectral image (HSI-MSI) fusion relies on a robust degradation model
and data prior, where the former describes the degeneration of HSI in the spectral and …
and data prior, where the former describes the degeneration of HSI in the spectral and …