scholarly journals An Automatic Sparse Pruning Endmember Extraction Algorithm with a Combined Minimum Volume and Deviation Constraint

2018 ◽  
Vol 10 (4) ◽  
pp. 509 ◽  
Author(s):  
Huali Li ◽  
Jun Liu ◽  
Haicong Yu
Keyword(s):  
Minimum Volume ◽  
Endmember Extraction ◽  
Extraction Algorithm

Selection of Hyperspectral Endmember Extraction Algorithm for Tumor Delineation in Animal Models

2021 ◽  
Author(s):  
Deependra Mishra ◽  
John Wang ◽  
Steven T. Wang ◽  
Qian Cao ◽  
Helena Hurbon ◽  
...  
Keyword(s):  
Animal Models ◽  
Endmember Extraction ◽  
Tumor Delineation ◽  
Extraction Algorithm ◽  
Selection Of

Endmember Extraction Using Minimum Volume and Information Constraint Nonnegative Matrix Factorization

2019 ◽  
Vol 16 (9) ◽  
pp. 1427-1431
Author(s):  
Yue Shi ◽  
Hongqi Wang ◽  
Xinyi Guo ◽  
Guangluan Xu
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Minimum Volume ◽  
Endmember Extraction

A Fast Endmember Extraction Algorithm Based on Gram Determinant

2014 ◽  
Vol 11 (6) ◽  
pp. 1124-1128 ◽  
Author(s):  
Kang Sun ◽  
Xiurui Geng ◽  
Panshi Wang ◽  
Yongchao Zhao
Keyword(s):  
Endmember Extraction ◽  
Extraction Algorithm ◽  
Gram Determinant

Group endmember extraction algorithm based on Gram–Schmidt orthogonalization

2019 ◽  
Vol 13 (02) ◽  
pp. 1
Author(s):  
Yan Zhao ◽  
Zhen Zhou ◽  
Donghui Wang
Keyword(s):  
Endmember Extraction ◽  
Extraction Algorithm ◽  
Schmidt Orthogonalization

A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery

2013 ◽  
Vol 79 ◽  
pp. 211-218 ◽  
Author(s):  
Xiurui Geng ◽  
Zhengqing Xiao ◽  
Luyan Ji ◽  
Yongchao Zhao ◽  
Fuxiang Wang
Keyword(s):  
Hyperspectral Imagery ◽  
Gaussian Elimination ◽  
Endmember Extraction ◽  
Extraction Algorithm

scholarly journals The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval

2020 ◽  
Vol 12 (22) ◽  
pp. 3693
Author(s):  
Hongyu Zhao ◽  
Xiaohua Hao ◽  
Jian Wang ◽  
Hongyi Li ◽  
Guanghui Huang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Absolute Error ◽  
Satellite Observation ◽  
Landsat 8 ◽  
Endmember Extraction ◽  
Analysis Model ◽  
Fractional Snow Cover ◽  
Extraction Algorithm ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

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