scholarly journals Subpixel Mapping of Hyperspectral Images Using a Labeled-Unlabeled Hybrid Endmember Library and Abundance Optimization

2020 ◽  
Vol 13 ◽  
pp. 5036-5047
Author(s):  
Yifan Zhang ◽  
Ting Wang ◽  
Shaohui Mei ◽  
Qian Du
Keyword(s):  
Hyperspectral Images ◽  
Subpixel Mapping

A New Genetic Method for Subpixel Mapping Using Hyperspectral Images

2016 ◽  
Vol 9 (9) ◽  
pp. 4480-4491 ◽  
Author(s):  
Xiaohua Tong ◽  
Xiong Xu ◽  
Antonio Plaza ◽  
Huan Xie ◽  
Haiyan Pan ◽  
...  
Keyword(s):  
Hyperspectral Images ◽  
Genetic Method ◽  
Subpixel Mapping

Multiobjective Subpixel Mapping With Multiple Shifted Hyperspectral Images

2020 ◽  
Vol 58 (11) ◽  
pp. 8176-8191
Author(s):  
Mi Song ◽  
Yanfei Zhong ◽  
Ailong Ma ◽  
Xiong Xu ◽  
Liangpei Zhang
Keyword(s):  
Hyperspectral Images ◽  
Subpixel Mapping

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Edge Preserving ◽  
Improvement Factor ◽  
Filter Output ◽  
Average Improvement ◽  
Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

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