Results 61 to 70 of about 77,969 (223)

Contrastive Learning approach for blind Hyperspectral Unmixing (CLHU)

, 2023
The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input ...
Himar Fabelo (16724360), Hiroshi Takemura (16724369), Gustavo M. Callico (16724362), Toshihiro Takamatsu (16724366), Abian Hernandez-Guedes (16724310), Nobuyoshi Takeshita (17326967), Hiro Hasegawa (17326970), samuel ortega (16724358), Juan Ruiz-Alzola (16724357), Ryodai Fukushima (16724364)   +9 more
core   +1 more source

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be
J. Bieniarz, D. Cerra, J. Avbelj, P. Reinartz, R. Müller   +4 more
doaj   +1 more source

A Digital Atlas of Human Ultrastructure

Small, EarlyView.
An interactive resource with ultrastructural data of human organs. Analyse organs from sub‐millimeter up to the nanometer range of macromolecules in a similar way as Google‐earth: https://www.nanotomy.org/OA/HumanAtlas/. ABSTRACT Electron microscopy (EM) is the method to visualize the building blocks of life in organs and tissues at biomolecular ...
Anouk H. G. Wolters, Kim Kats, Jeroen Kuipers, Erik Bleuel, Ben N. G. Giepmans   +4 more
wiley   +1 more source

KPI‐NeRF: Hyperspectral Neural Radiance Fields from a Single Kaleidoscopic Plenoptic Image

Computer Graphics Forum, EarlyView.
KPI‐NeRF reconstructs hyperspectral light field images from a single kaleidoscopic plenoptic image. By leveraging neural radiance fields, it enables high‐quality spectral and angular de‐multiplexing for dynamic scene capture. Abstract Plenoptic imaging has excessive sampling requirements associated with the high dimensionality of the desired data.
Erqi Huang, John Restrepo, Xun Cao, Ivo Ihrke   +3 more
wiley   +1 more source

Robust linear unmixing with enhanced constraint of classification for hyperspectral remote sensing imagery

IET Image Processing, 2022
Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors.
Haoyang Yu, Jinxue Chi, Xiaodi Shang, Xueji Shen, Jocelyn Chanussot, Yimin Shi   +5 more
doaj   +1 more source

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe, Yazhou Sun, Sanai Li, Wenxuan Guo   +3 more
wiley   +1 more source

Super‐Multiplexed Label‐Free Raman Imaging Uncovers Novel Testicular Metabolic Couplings for Residual Body and Spermatogonial Differentiation

Advanced Science, Volume 13, Issue 58, 19 October 2026.
Super‐multiplexed Label‐free Raman Imaging (SLRI) enables 2D/3D metabolic mapping of intact Drosophila testes. Moving beyond descriptive morphology, it establishes a multidimensional tool for tissue metabolic remodeling, and offers a generalizable platform for complex tissue analysis, with implications extending to development and disease. ABSTRACT The
Jiaxin Li, Xia Chen, Yujuan Qi, Chen Qiao, Qiuru Huang, Dan Zhao, Weiyi Qian, Li Di, Yi Shi, Xiangqi Hu, Yueyue Shao, Kehan Wang, Zhiyu Tang‐Yang, Hanzhi Zang, Yuwen Du, Anran Dong, Jinxiong Feng, Jiayun Chen, Ying Zheng, Xiaofang Tan, Xiaoyu Zhao, Zhonghua Shi, Bo Zheng, Jun Yu   +23 more
wiley   +1 more source

LiDAR-driven spatial regularization for hyperspectral unmixing [PDF]

, 2018
International audienceOnly a few research works consider LiDAR data while conducting hyperspectral image unmixing. However, the digital surface model derived from LiDAR can provide meaningful information, in particular when spatially regularizing the ...
Uezato, Tatsumi, Dobigeon, Nicolas, Fauvel, Mathieu   +2 more
core   +3 more sources

Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical challenge in the analysis of hyperspectral remote sensing data. Due to the complex interactions between incident light and materials, which are significantly influenced by the three-dimensional geometry of the scene ...
Xiu Zhao, Meiping Song
doaj   +1 more source

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Remote Sensing, 2017
Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional ...
Ruyi Feng, Yanfei Zhong, Lizhe Wang, Wenjuan Lin   +3 more
doaj   +1 more source