Noise removal for airborne time domain electromagnetic data based on minimum noise fraction

2018 ◽  
Vol 49 (2) ◽  
pp. 127-133
Author(s):  
Yue Li ◽  
Yang Meng ◽  
Yiming Lu ◽  
Lingqun Wang ◽  
Bin Xie ◽  
...  
Keyword(s):  
Time Domain ◽  
Noise Removal ◽  
Electromagnetic Data ◽  
Minimum Noise Fraction ◽  
Time Domain Electromagnetic ◽  
Noise Fraction ◽  
Minimum Noise

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

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