Results 21 to 30 of about 1,373 (228)

Improved Hyperspectral Unmixing with Endmember Variability Parametrized Using an Interpolated Scaling Tensor [PDF]

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Endmember (EM) variability has an important impact on the performance of hyperspectral image (HI) analysis algorithms. Recently, extended linear mixing models have been proposed to account for EM variability in the spectral unmixing (SU) problem. The direct use of these models has led to severely ill-posed optimization problems.
Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez   +2 more
openaire   +3 more sources

Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments

Remote Sensing, 2016
Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel ...
Yingbin Deng, Changshan Wu
doaj   +2 more sources

A multiple endmember mixing model to handle spectral variability

, 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 ...
Uezato, Tatsumi, Fauvel, Mathieu, Dobigeon, Nicolas   +2 more
openaire   +3 more sources

Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery

Remote Sensing
We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM).
John Waczak, David J. Lary
doaj   +2 more sources

Bayesian Subpixel Mapping of Hyperspectral Imagery via Discrete Endmember Variability Mixture Model and Markov Random Field

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Although Bayesian methods have been very effective for spatial–spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced subpixel mapping (SPM) by simultaneously considering several key issues, i.e., endmember
Yujia Chen, Rongming Zhuo, Linlin Xu, Junhuan Peng, Xiaoman Qi, Zhaoxu Zhang, Zhongzheng Hu   +6 more
doaj   +1 more source

Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

Geo-spatial Information Science, 2022
Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images.
Zhenfeng Shao, Yuan Zhang, Cheng Zhang, Xiao Huang, Tao Cheng   +4 more
doaj   +1 more source

A Novel Hyperspectral Unmixing Method based on Least Squares Twin Support Vector Machines

European Journal of Remote Sensing, 2021
In hyperspectral images, endmembers characterizing one class of ground object may vary due to illumination, weathering, slight variations of the materials.
Liguo Wang, San Wang, Xiuping Jia, Tianyi Bi   +3 more
doaj   +1 more source

Variability of the endmembers in spectral unmixing: Recent advances [PDF]

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account.
Drumetz, Lucas, Chanussot, Jocelyn, Jutten, Christian   +2 more
openaire   +2 more sources

Generalized Linear Mixing Model Accounting for Endmember Variability [PDF]

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from ...
Tales Imbiriba, Ricardo Augusto Borsoi, José Carlos Moreira Bermudez   +2 more
openaire   +2 more sources

VALIDATION OF EXTRACTED ENDMEMBERS FROM HYPERSPECTRAL IMAGES [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
An essential step in the characterization of surface materials using hyperspectral image analysis is image classification using endmembers. Spectral unmixing is the best method for hyperspectral image classification.
A. Sharifi, M. Hosseingholizadeh
doaj   +1 more source