An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing [PDF]
Remote Sensing, 2021 With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing.
Chuanlong Ye +5 more
doaj +4 more sources
Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis [PDF]
Remote Sensing, 2015 As an important indicator of anthropogenic impacts on the Earth’s surface, it is of great necessity to accurately map large-scale urbanized areas for various science and policy applications.
Chengbin Deng
doaj +4 more sources
Hyperspectral unmixing accounting for spatial correlations and endmember variability [PDF]
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015 This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances.
Halimi, Abderrahim +3 more
core +13 more sources
Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction [PDF]
Applied Sciences, 2022 The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the ...
Xiangyue Zhang, Yueming Wang, Tianru Xue
doaj +2 more sources
A multiple endmember mixing model to handle spectral variability in hyperspectral unmixing [PDF]
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018 This paper proposes a novel mixing model that incorporates spectral variability. The proposed approach relies on the following two ingredients: i) a mixed spectrum is modeled as a combination of a few endmember signatures which belong to some endmember bundles (referred to as classes), ii) sparsity is promoted for the selection of both endmember ...
Tatsumi Uezato +2 more
core +5 more sources
Unmixing multitemporal hyperspectral images accounting for endmember variability [PDF]
2015 23rd European Signal Processing Conference (EUSIPCO), 2015 Publication in the conference proceedings of EUSIPCO, Nice, France ...
Halimi, Abderrahim +4 more
core +9 more sources
Sum-Product Unmixing for Hyperspectral Analysis With Endmember Variability [PDF]
IEEE Geoscience and Remote Sensing Letters, 2018 Models of endmember variability capture the notion that multiple spectra may represent a single class or material, and while these models are physically realistic, they often give rise to excessive computational complexity during the spectral unmixing process.
Charan Puladas +2 more
openaire +4 more sources
Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2021 Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinearly related to the pure material endmember spectra, making it hard to estimate the fractional abundances of the materials. Changing illumination conditions and cross-sensor situations cause spectral variability, further complicating the unmixing ...
Bikram Koirala +3 more
openaire +3 more sources
The AMEE-PPI Method to Extract Typical Outcrop Endmembers from GF-5 Hyperspectral Images [PDF]
SensorsMixed pixels remain a central obstacle to reliable endmember extraction from hyperspectral imagery. We present AMEE–PPI, a hybrid method that embeds the Pure Pixel Index (PPI) within morphological structuring elements and propagates spectral purity via ...
Lin Hu +6 more
doaj +2 more sources
An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels [PDF]
Remote Sensing, 2015 Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA.
Yuanliu Xu, Jiancheng Shi, Jinyang Du
doaj +2 more sources