Maximin distance based band selection for endmember extraction in hyperspectral images using simplex growing algorithm

2017 ◽  
Vol 77 (6) ◽  
pp. 7221-7237 ◽  
Author(s):  
Veera Senthil Kumar Ganesan ◽  
Vasuki S
Keyword(s):  
Hyperspectral Images ◽  
Band Selection ◽  
Endmember Extraction ◽  
Selection For ◽  
Simplex Growing Algorithm ◽  
Maximin Distance

scholarly journals Band selection for hyperspectral images using probabilistic memetic algorithm

2014 ◽  
Vol 20 (12) ◽  
pp. 4685-4693 ◽  
Author(s):  
Liang Feng ◽  
Ah-Hwee Tan ◽  
Meng-Hiot Lim ◽  
Si Wei Jiang
Keyword(s):  
Memetic Algorithm ◽  
Hyperspectral Images ◽  
Band Selection ◽  
Selection For

scholarly journals Mixed-Noise Band Selection for Hyperspectral Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 173815-173825
Author(s):  
Zhen Li ◽  
Chenwei Deng ◽  
Yun Huang
Keyword(s):  
Hyperspectral Images ◽  
Band Selection ◽  
Noise Band ◽  
Mixed Noise ◽  
Selection For

Band selection for hyperspectral images based on impurity function

2011 ◽  
Author(s):  
Yang-Lang Chang ◽  
Bin-Feng Shu ◽  
Tung-Ju Hsieh ◽  
Chih-Yuan Chu ◽  
Jyh-Perng Fang
Keyword(s):  
Hyperspectral Images ◽  
Band Selection ◽  
Selection For

scholarly journals A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

2016 ◽  
Vol XLI-B7 ◽  
pp. 117-120
Author(s):  
S. Sharifi hashjin ◽  
A. Darvishi ◽  
S. Khazai ◽  
F. Hatami ◽  
M. Jafari houtki
Keyword(s):  
Target Detection ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Band Selection ◽  
Blind Test ◽  
Simple Method ◽  
Test Dataset ◽  
Detection Algorithms ◽  
Selection For ◽  
Field Reflectance

In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

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