Total Utility Metric based Dictionary Pruning for Sparse Hyperspectral Unmixing

2021 ◽  
pp. 1-1
Author(s):  
Sefa Kucuk ◽  
Seniha Yuksel
Keyword(s):  
Total Utility ◽  
Hyperspectral Unmixing

Spectral-Spatial Hyperspectral Unmixing in Transformed Domains

2020 ◽  
Author(s):  
Chenguang Xu ◽  
Shaoquan Zhang ◽  
Chengzhi Deng ◽  
Zhaoming Wu ◽  
Jiaheng Yang ◽  
...  
Keyword(s):  
Hyperspectral Unmixing

scholarly journals DLR HySU—A Benchmark Dataset for Spectral Unmixing

2021 ◽  
Vol 13 (13) ◽  
pp. 2559
Author(s):  
Daniele Cerra ◽  
Miguel Pato ◽  
Kevin Alonso ◽  
Claas Köhler ◽  
Mathias Schneider ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Benchmark Dataset ◽  
Data Sets ◽  
Endmember Extraction ◽  
Imaging Spectrometer ◽  
Hyperspectral Unmixing ◽  
Processing Step ◽  
German Aerospace ◽  
Spectrometer Data ◽  
First Time

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

Endmember independence constrained hyperspectral unmixing via nonnegative tensor factorization

2021 ◽  
Vol 216 ◽  
pp. 106657
Author(s):  
Jin-Ju Wang ◽  
Ding-Cheng Wang ◽  
Ting-Zhu Huang ◽  
Jie Huang ◽  
Xi-Le Zhao ◽  
...  
Keyword(s):  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
Hyperspectral Unmixing

Nonnegative matrix factorization with entropy regularization for hyperspectral unmixing

2021 ◽  
Vol 42 (16) ◽  
pp. 6362-6393
Author(s):  
Junmin Liu ◽  
Shuai Yuan ◽  
Xuehu Zhu ◽  
Yifan Huang ◽  
Qian Zhao
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Hyperspectral Unmixing
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