Results 1 to 10 of about 1,373 (228)

A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability [PDF]

Sensors, 2017
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the ...
Wenliang Li, Changshan Wu
doaj   +6 more sources

An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing

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, Shanwei Liu, Mingming Xu, Bo Du, Jianhua Wan, Hui Sheng   +5 more
doaj   +4 more sources

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability [PDF]

IEEE Transactions on Image Processing, 2015
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to take into account their variability in the image.
Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret   +2 more
exaly   +7 more sources

Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2016
In hyperspectral unmixing applications, one typically assumes that a single spectrum exists for every endmember. In many scenarios, this is not the case, and one requires a set or a distribution of spectra to represent an endmember or class. This inherent spectral variability can pose severe difficulties in classical unmixing approaches. In this paper,
Rob Heylen, Alina Zare, Paul Gader
exaly   +3 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

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

Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction

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

The AMEE-PPI Method to Extract Typical Outcrop Endmembers from GF-5 Hyperspectral Images [PDF]

Sensors
Mixed 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, Jiankai Hu, Shu Gan, Xiping Yuan, Yu Lu, Hailong Zhao, Guang Han   +6 more
doaj   +2 more sources

A Hierarchical Sparsity Unmixing Method to Address Endmember Variability in Hyperspectral Image [PDF]

Remote Sensing, 2018
With a low spectral resolution hyperspectral sensor, the signal recorded from a given pixel against the complex background is a mixture of spectral contents. To improve the accuracy of classification and subpixel object detection, hyperspectral unmixing (
Jinlin Zou, Jinhui Lan, Yang Shao
doaj   +2 more sources

A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing

IEEE Transactions on Image Processing, 2018
Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model (NCM), where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions. However, in real applications, the distribution of a material is often not Gaussian.
Yuan Zhou, Anand Rangarajan, Paul D Gader   +2 more
exaly   +5 more sources

Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands [PDF]

Remote Sensing, 2017
Variable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands
Omid Ghaffari, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade   +2 more
doaj   +2 more sources