Results 111 to 120 of about 11,517,403 (217)
Nonlinear spectral unmixing of hyperspectral images using Gaussian processes [PDF]
, 2012 This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components ...
Altmann, Yoann +5 more
core +1 more source
Surgical hyperspectral imaging: a systematic review
Computer Assisted SurgeryHyperspectral imaging (HSI) is a technique that captures and processes information across a wide spectrum of wavelengths, providing detailed spectral data for each pixel in an image to identify and analyze materials or objects. In the surgical domain, it
Hafsa Moontari Ali +2 more
doaj +1 more source
Journal of Food Safety, Volume 46, Issue 5, October 2026.Hyperspectral imaging and an optimized stacking ensemble learning model are developed for rapid, nondestructive toxicological assessment of fish meat. This framework integrated multiple machine learning classifiers to accurately distinguish toxicologically contaminated fish from fresh samples using both reduced (25‐band) and full (224‐band) spectral ...
Muhammad Aqeel +5 more
wiley +1 more source
mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification
CoRRRecently, DeepSeek has invented the manifold-constrained hyper-connection (mHC) approach which has demonstrated significant improvements over the traditional residual connection in deep learning models \cite{xie2026mhc}. Nevertheless, this approach has not been tailor-designed for improving hyperspectral image (HSI) classification.
Yimin Zhu 0002 +8 more
openaire +3 more sources
SRS: 3D Mapping and Hyperspectral Imaging (HSI) v1
We have developed a protocol for utilizing stimulated Raman scattering (SRS) to acquirelabel-free measurements of specific macromolecules. When performing SRS imagingwe can acquire total protein, total lipid, unsaturated lipid, and saturatedlipid concentration from distinct Raman shift wavenumbers.
Jorge Villazon +6 more
openaire +1 more source
Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging [PDF]
, 2016 Hyperspectral imaging (HSI) is used in a wide range of applications such as remote sensing, space imagery, mineral detection, and exploration. Unfortunately, it is difficult to acquire hyperspectral images with high spatial and spectral resolution due to
Vargas, Cesar Augusto +10 more
core +1 more source
Hyperspectral and multispectral image fusion via tensor sparsity regularization [PDF]
, 2019 Hyperspectral image (HSI) super-resolution scheme based on HSI and multispectral image (MSI) fusion has been a prevalent research theme in remote sensing.
Liao, WenzhiTW070000918519268020008033180000-0002-2183-03242434B67E-F0EE-11E1-A9DE-61C894A0A6B4 +6 more
core
A low-rank tensor decomposition based hyperspectral image compression algorithm
, 2016 Hyperspectral image (HSI), which is widely known that contains much richer information in spectral domain, has attracted increasing attention in various fields. In practice, however, since a hyperspectral image itself contains large amount of redundant
Bo Du +8 more
core +1 more source
Hyperspectral Image Classification with Multi-Scale Feature Extraction
, 2019 Spectral features cannot effectively reflect the differences among the ground objects and distinguish their boundaries in hyperspectral image (HSI) classification.
Danbing He +4 more
core +1 more source
Image Fusion for Hyperspectral Image Super-Resolution
, 2018 Hyperspectral sensors have high spectral resolution by capturing images in hundreds of bands. Despite the high spectral resolution, low spatial resolution of these sensors restricts the performance of the hyperspectral imaging applications such as target
Akar, Gözde +5 more
core +1 more source