Results 51 to 60 of about 265 (159)

Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations [PDF]

Earth System Science Data, 2022
Using data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station between 2010–2014, hyperspectral reflectance spectra of various floating matters in global oceans and lakes are derived for the spectral ...
C. Hu
doaj   +1 more source

A comparative study of hyperspectral unmixing using different algorithm approaches

, 2020
Hyperspectral unmixing (HU) is an important technique for remotely sensed hyperspectral data exploitation. Hyperspectral unmixing is required to get an accurate estimation due to low spatial resolution of hyperspectral cameras, microscopic material ...
Majid Darsono, Abdul, Manap, Redzuan Abdul, Mohd Yusof, Ain Zat, Abdul Manap, Redzuan, Yusof, Ain Zat Mohd, Darsono, Abd Majid   +5 more
core   +1 more source

AutoNAS: Automatic Neural Architecture Search for Hyperspectral Unmixing

, 2022
International audienceDue to the powerful and automatic representation capabilities, deep learning (DL) techniques have made significant breakthroughs and progress in hyperspectral unmixing (HU).
Hong, Danfeng, Zhang, Bing, Gao, Lianru, Chanussot, Jocelyn, Han, Zhu, Huang, Min   +5 more
core   +1 more source

Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing [PDF]

, 2021
Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and
Meng, Deyu, Hong, Danfeng, Xu, Zongben, Xu, Lin, Yao, Jing, Chanussot, Jocelyn   +5 more
core   +1 more source

Multiscale Convolutional Mask Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep learning has gained popularity in hyperspectral unmixing (HU) applications recently due to its powerful learning and data-fitting capabilities. As an unmixing baseline network, the autoencoder (AE) framework performs well in HU by automatically ...
Mingming Xu, Jin Xu, Shanwei Liu, Hui Sheng, Zhiru Yang   +4 more
doaj   +1 more source

Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery

, 2023
Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries.
Min Hu, Fan Li, Chengzhi Deng, Dingli Su, Shaoquan Zhang, Shengqian Wang, Pengfei Lai, Yonggang Chen   +7 more
core   +1 more source

Spectral-spatial adversarial network for nonlinear hyperspectral unmixing of imbalanced datasets

International Journal of Applied Earth Observations and Geoinformation, 2023
With its successful application in various fields, hyperspectral unmixing (HU) technology has received extensive attention in remote sensing processing. Recently, various autoencoders based on the linear mixing model (LMM) have been proposed to provide a
Xu Yang, Jianguo Chen, Zihao Chen
doaj   +1 more source

Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science

Modern Agriculture, Volume 3, Issue 2, December 2025.
Hyperspectral imaging (HSI) has emerged as a powerful non‐destructive technique for characterisation of the plant phenotype and physiological traits. The ongoing development of cost‐effective hardware, coupled with standardised acquisition protocols and open‐access spectral libraries, is accelerating its integration with multi‐omics approaches to ...
Jingyan Song, Haifeng Liang, Bingjie Lu, Jing Guo, Yuan Gao, Xiao Hu, Manlin Yang, Xiaofan Li, Zhenyu Wang, Yongqi Chen, Yinyin Zhang, Shen Su, Zhangyun Gao, Shijie Li, Ping Chen, Jing Wang, Wanneng Yang, Hui Feng   +17 more
wiley   +1 more source

An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures

IEEE Access, 2019
Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials' signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene.
Yao-Rong Syu, Chia-Hsiang Lin, Chong-Yung Chi   +2 more
doaj   +1 more source

Fast and Structured Block-Term Tensor Decomposition for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
The block-term tensor decomposition model with multilinear rank-$(L_{r},L_{r},1)$ terms (or the “${\mathsf{LL1}}$ tensor decomposition” in short) offers a valuable alternative formulation for hyperspectral unmixing (HU), which ensures the ...
Meng Ding, Xiao Fu, Xi-Le Zhao
doaj   +1 more source