Results 61 to 70 of about 296,705 (175)

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

Remote Sensing, 2017
In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation.
Elham Kordi Ghasrodashti, Azam Karami, Rob Heylen, Paul Scheunders   +3 more
doaj   +1 more source

Enhancing Identifiable Variational Autoencoder in the Presence of Missing Values and Auxiliary Covariates for Spatio‐Temporal Ozone Modeling

Environmetrics, Volume 37, Issue 6, September 2026.
ABSTRACT The formation of ground‐level ozone follows complex nonlinear photochemical processes that depend on multiple environmental factors and have strong spatio‐temporal structures. Environmental data used to study these dynamics usually originate from multiple sources, including in situ monitoring stations and satellite observations.
Mika Sipilä, Sandra De Iaco, Claudia Cappello, Klaus Nordhausen, Monica Palma, Sara Taskinen   +5 more
wiley   +1 more source

A New Fast Sparse Unmixing Algorithm Based on Adaptive Spectral Library Pruning and Nesterov Optimization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, hyperspectral sparse unmixing (HSU) has garnered extensive research and attention due to its unique characteristic of not requiring the estimation of endmembers and their number.
Kewen Qu, Fangzhou Luo, Huiyang Wang, Wenxing Bao   +3 more
doaj   +1 more source

Improved Collaborative Non-Negative Matrix Factorization and Total Variation for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing (HSU) is an important technique of remote sensing, which estimates the fractional abundances and the mixing matrix of endmembers in each mixed pixel from the hyperspectral image.
Yuan Yuan, Zihan Zhang, Qi Wang
doaj   +1 more source

Stationary Subspace Analysis for Spatial Data

Environmetrics, Volume 37, Issue 6, September 2026.
ABSTRACT Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial stationary subspace analysis (spSSA), which explicitly accounts for spatial dependence.
Perttu Saarela, Klaus Nordhausen, Jaakko Pere, Anne M. Ruiz   +3 more
wiley   +1 more source

Abundance Estimation Methods in Spectral Unmixing for Real Data [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Spectral unmixing estimates the fractional abundances of materials, having associated spectra called endmembers, in pixels acquired by imaging spectrometers.
D. Cerra, M. Pato, E. Carmona
doaj   +1 more source

Unmixing of Hyperspectral Data Using Spectral Libraries

International Journal of Environment and Geoinformatics, 2020
In hyperspectral images, pixels are found as a mixture of the spectral signatures of several materials, especially when there is an insufficient spatial resolution.
Sefa Küçük, Seniha Esen Yüksel
doaj   +1 more source

Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF

IEEE Access, 2021
Due to the low spatial resolution of the sensors, the hyperspectral images contain mixed pixels. The purpose of hyperspectral unmixing is to decompose the mixed pixels into a series of endmembers and abundance fractions.
Jing Liu, You Zhang, Yi Liu, Caihong Mu
doaj   +1 more source

Establishing Monthly Benchmarks for Erosion Control and Vegetation Restoration Targets Across the Loess Plateau

Land Degradation &Development, Volume 37, Issue 15, Page 11500-11515, September 2026.
ABSTRACT Establishing scientifically grounded soil erosion control targets and feasible vegetation restoration strategies is crucial for high quality development of the Chinese Loess Plateau which is severely affected by soil erosion. Currently, the commonly used tolerable soil loss (TSL) is inadequate for addressing the spatial heterogeneity in ...
Ye Wang, Haijing Shi, Xihua Yang, Rui Li, Hongming Zhang, Jiaqiong Zhang, Wenyi Sun, Yangyang Liu, Qinggaozi Zhu, Guang Yang   +9 more
wiley   +1 more source

Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion [PDF]

, 2011
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.
Avbelj, Janja, Reinartz, Peter, Müller, Rupert, Cerra, Daniele, Bieniarz, Jakub   +4 more
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