Results 101 to 110 of about 9,466 (207)

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition.
Chong Zhao, Jinlin Wang, Qingqing Qiao, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Dong Li, Tao Liao, Chao Li, Heshun Qiu, Guangjun Qu   +11 more
doaj   +1 more source

Hyperspectral Unmixing With Multi-Scale Convolution Attention Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance.
Sheng Hu, Huali Li
doaj   +1 more source

Mapping Peatlands Combing Deep Learning With Sparse Spectral Unmixing Based on Zhuhai-1 Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping.
Yulin Xu, Xiaodong Na
doaj   +1 more source

Tissue Classification of Breast Cancer by Hyperspectral Unmixing. [PDF]

Cancers (Basel), 2023
Jong LS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B.   +6 more
europepmc   +1 more source

Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case

, 2015
In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where
Ambikapathi, ArulMurugan, Chi, Chong-Yung, Li, Wei-Chiang, Lin, Chia-Hsiang, Ma, Wing-Kin   +4 more
core   +1 more source

Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning

IEEE Access, 2019
Hyperspectral unmixing is an important and challenging task in the field of remote sensing which arises when the spatial resolution of sensors is insufficient for the separation of spectrally distinct materials.
Burkni Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson   +2 more
doaj   +1 more source

Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing. [PDF]

Sensors (Basel), 2022
Jia X, Guo B.
europepmc   +1 more source

A Multiple Hypothesis Testing Approach to Low-Complexity Subspace Unmixing [PDF]

, 2016
Subspace-based signal processing traditionally focuses on problems involving a few subspaces. Recently, a number of problems in different application areas have emerged that involve a significantly larger number of subspaces relative to the ambient ...
Bajwa, Waheed U., Mixon, Dustin G.
core  

Updated Homogeneity Criteria Based Low-Dimensional Representation for Hyperspectral Unmixing

IEEE Access
Superpixel-based approaches have been proposed for hyperspectral unmixing. The basic assumption of this approach is that the superpixel over-segmentation segments the image into small homogeneous areas.
Jiarui Yi, Huiyi Gao
doaj   +1 more source

A Global-to-Local Spectral-Spatial Attention-Based Nonlinearity and Scaled Endmember Variability Parametric Learning Network for Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing has attracted increasing attention in remote sensing applications. Unfortunately, significant unmixing residuals often arise from the coupled nonlinear mixing effects and spectral variability (SV), bringing challenges for reliably ...
Yi Zhao, Bin Yang
doaj   +1 more source